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We show that the fundamental legal structure of a well-written financial contract follows a state-transition logic that can be formalized mathematically as a finite-state machine (specifically, a deterministic finite automaton or DFA). The automaton defines the states that a financial relationship can be in, such as “default,” “delinquency,” “performing,” etc., and it defines an “alphabet” of events that can trigger state transitions, such as “payment arrives,” “due date passes,” etc. The core of a contract describes the rules by which different sequences of events trigger particular sequences of state transitions in the relationship between the counterparties. By conceptualizing and representing the legal structure of a contract in this way, we expose it to a range of powerful tools and results from the theory of computation. These allow, for example, automated reasoning to determine whether a contract is internally coherent and whether it is complete relative to a particular event alphabet. We illustrate the process by representing a simple loan agreement as an automaton.

The need to address the challenge of vagueness across several domains of applicability of ontology is gaining research attention. The presence of vagueness in knowledge represented with description logic impairs automating reasoning and inference making. The importance of reducing this vagueness in the formalization of medical knowledge representation is rising, considering the vulnerability of this domain to the expression of vague concepts or terms. This vagueness may be addressed from the perspective of ontology modeling language application such as ontology web language (OWL). Although several attempts have been made to tackle this problem in other disease prognoses such as diabetes and cardiovascular diseases, a similar effort is missing for breast cancer. Minimizing vagueness in breast cancer ontology is necessary to enhance automated reasoning and handle knowledge representation problems. This study proposes a framework for reducing vagueness in breast cancer ontology. The approach obtained breast cancer crisp ontology and applied fuzzy ontology elements based on the Fuzzy OWL2 model to formulate breast cancer fuzzy ontology. This was achieved by extending the elements of OWL2 (a more expressive version of OWL) with annotation properties to fuzzify the breast cancer crisp ontology. Results obtained showed a significant reduction of vagueness in the domain, yielding 0.38 for vagueness spread and 1.0 for vagueness explicitness. In addition, ontology metrics such as completeness, consistency, correctness and accuracy were also evaluated, and we obtained impressive performance. The implication of this result is the reduction of vagueness in breast cancer ontology, which provides increased computational reasoning support to applications using the ontology.

The past 25 years have seen many attempts to introduce defeasible-reasoning capabilities into a description logic setting. Many, if not most, of these attempts are based on preferential extensions of description logics, with a significant number of these, in turn, following the so-called KLM approach to defeasible reasoning initially advocated for propositional logic by Kraus, Lehmann, and Magidor. Each of these attempts has its own aim of investigating particular constructions and variants of the (KLM-style) preferential approach. Here our aim is to provide a comprehensive study of the formal foundations of preferential defeasible reasoning for description logics in the KLM tradition. We start by investigating a notion of defeasible subsumption in the spirit of defeasible conditionals as studied by Kraus, Lehmann, and Magidor in the propositional case. In particular, we consider a natural and intuitive semantics for defeasible subsumption, and we investigate KLM-style syntactic properties for both preferential and rational subsumption. Our contribution includes two representation results linking our semantic constructions to the set of preferential and rational properties considered. Besides showing that our semantics is appropriate, these results pave the way for more effective decision procedures for defeasible reasoning in description logics. Indeed, we also analyse the problem of non-monotonic reasoning in description logics at the level of entailment and present an algorithm for the computation of rational closure of a defeasible knowledge base. Importantly, our algorithm relies completely on classical entailment and shows that the computational complexity of reasoning over defeasible knowledge bases is no worse than that of reasoning in the underlying classical DL ALC .

Software tools of Automated Reasoning are too sophisticated for general use in mathematics education and respective reasoning, while Lucas-Interpretation provides a general concept for integrating such tools into educational software with the purpose to reliably and flexibly check formal input of students. This paper gives the first technically concise description of Lucas-Interpretation at the occasion of migrating a prototype implementation to the function package of the proof assistant Isabelle. The description shows straightforward adaptations of Isabelle's programming language and shows, how simple migration of the interpreter was, since the design (before the function package has been introduced to Isabelle) recognised appropriateness of Isabelle's terms as middle end. The paper gives links into the code in an open repository as invitation to readers for re-using the prototyped code or adopt the general concept. And since the prototype has been designed before the function package was implemented, the paper is an opportunity for recording lessons learned from Isabelle's development of code structure., Comment: In Proceedings ThEdu'20, arXiv:2010.15832

Human activity recognition has been a popular research area concerned with identifying the specific movement or action of a person based on variety of sensor data. Conventional human activity recognition approaches are mainly data driven, which are not working well for composite activity recognition due to the complexity and uncertainty of real scenarios. We propose in this paper a hierarchical structure-based framework and methodology for human activity recognition by an integration of data-driven approach and knowledge-based approach, which provides an interesting framework capable of bridging lower-level pattern recognition and higher-level knowledge for reasoning and explanation. More specifically, this approach constructs a hierarchical structure for representing the composite activity by a composition of lower-level actions and gestures according to its semantic meaning. This hierarchical structure is then transformed into formal syntactical logical formulas and rules, based on which the resolution based automated reasoning is applied to recognize the composite activity given the recognized lower-level actions by using data driven machine learning methods. The work is the validated using some open-source data about video based human activity recognition. The present work provides a promising framework and application illustration of integration of machine learning and symbolic reasoning.

The design of new devices and experiments in science and engineering has historically relied on the intuitions of human experts. This credo, however, has changed. In many disciplines, computer-inspired design processes, also known as inverse-design, have augmented the capability of scientists. Here we visit different fields of physics in which computer-inspired designs are applied. We will meet vastly diverse computational approaches based on topological optimization, evolutionary strategies, deep learning, reinforcement learning or automated reasoning. Then we draw our attention specifically on quantum physics. In the quest for designing new quantum experiments, we face two challenges: First, quantum phenomena are unintuitive. Second, the number of possible configurations of quantum experiments explodes combinatorially. To overcome these challenges, physicists began to use algorithms for computer-designed quantum experiments. We focus on the most mature and \textit{practical} approaches that scientists used to find new complex quantum experiments, which experimentalists subsequently have realized in the laboratories. The underlying idea is a highly-efficient topological search, which allows for scientific interpretability. In that way, some of the computer-designs have led to the discovery of new scientific concepts and ideas -- demonstrating how computer algorithm can genuinely contribute to science by providing unexpected inspirations. We discuss several extensions and alternatives based on optimization and machine learning techniques, with the potential of accelerating the discovery of practical computer-inspired experiments or concepts in the future. Finally, we discuss what we can learn from the different approaches in the fields of physics, and raise several fascinating possibilities for future research., Comment: Comments and suggestions for additional references are welcome!

This chapter attempts to measure the motor learning skill of subjects using 2 well‐known brain signals, called error‐related potential (ErrP) and N400. It aims to develop a hybrid brain–computer interfacing system with a motivation to detect motor execution failures. The proposed motor learning skill assessment includes three steps: feature extraction of the used electroencephalography (EEG) signals, detection of the EEG signals based on the selected features, and automated reasoning to measure the degree of learning skill of the driving learners in motor learning activity. The chapter provides a general overview of the problem along with the design of the fuzzy rules and the approach is undertaken. It concerns the inference generation procedures by the proposed type‐2 fuzzy reasoning to determine the degree of learning of the desired motor actuation. The chapter deals the experiments undertaken and results obtained thereof and provides performance analysis and statistical validation.

Marketization and quantification have become ingrained in academia over the past few decades. The trust in numbers and incentives has led to a proliferation of devices that individualize, induce, benchmark, and rank academic performance. As an instantiation of that trend, this article focuses on the establishment and contestation of ‘algorithmic allocation’ at a Dutch university medical centre. Algorithmic allocation is a form of data-driven automated reasoning that enables university administrators to calculate the overall research budget of a department without engaging in a detailed qualitative assessment of the current content and future potential of its research activities. It consists of a range of quantitative performance indicators covering scientific publications, peer recognition, PhD supervision, and grant acquisition. Drawing on semi-structured interviews, focus groups, and document analysis, we contrast the attempt to build a rationale for algorithmic allocation—citing unfair advantage, competitive achievement, incentives, and exchange—with the attempt to challenge that rationale based on existing epistemic differences between departments. From the specifics of the case, we extrapolate to considerations of epistemic and market fairness that might equally be at stake in other attempts to govern the production of scientific knowledge in a quantitative and market-oriented way.Keywords algorithmic allocation; higher education; marketization; performance indicators; quantification; resource allocation

Probabilistic machine learning enabled by the Bayesian formulation has recently gained significant attention in the domain of automated reasoning and decision-making. While impressive strides have been recently made to scale up the performance of deep Bayesian neural networks, they have been primarily standalone software efforts without any regard to the underlying hardware implementation. In this article, we propose an “all-spin” Bayesian neural network where the underlying spintronic hardware provides a better match to the Bayesian computing models. To the best of our knowledge, this is the first exploration of a Bayesian neural hardware accelerator enabled by emerging post-CMOS technologies. We develop an experimentally calibrated device-circuit-algorithm cosimulation framework and demonstrate $24\times $ reduction in energy consumption against an iso-network CMOS baseline implementation.

Background Ontologies are widely used across biology and biomedicine for the annotation of databases. Ontology development is often a manual, time-consuming, and expensive process. Automatic or semi-automatic identification of classes that can be added to an ontology can make ontology development more efficient. Results We developed a method that uses machine learning and word embeddings to identify words and phrases that are used to refer to an ontology class in biomedical Europe PMC full-text articles. Once labels and synonyms of a class are known, we use machine learning to identify the super-classes of a class. For this purpose, we identify lexical term variants, use word embeddings to capture context information, and rely on automated reasoning over ontologies to generate features, and we use an artificial neural network as classifier. We demonstrate the utility of our approach in identifying terms that refer to diseases in the Human Disease Ontology and to distinguish between different types of diseases. Conclusions Our method is capable of discovering labels that refer to a class in an ontology but are not present in an ontology, and it can identify whether a class should be a subclass of some high-level ontology classes. Our approach can therefore be used for the semi-automatic extension and quality control of ontologies. The algorithm, corpora and evaluation datasets are available at https://github.com/bio-ontology-research-group/ontology-extension.

Collaboration across institutional boundaries is widespread and increasing today. It depends on federations sharing data that often have governance rules or external regulations restricting their use. However, the handling of data governance rules (aka. data-use policies) remains manual, time-consuming and error-prone, limiting the rate at which collaborations can form and respond to challenges and opportunities, inhibiting citizen science and reducing data providers' trust in compliance. Using an automated system to facilitate compliance handling reduces substantially the time needed for such non-mission work, thereby accelerating collaboration and improving productivity. We present a framework, Dr.Aid, that helps individuals, organisations and federations comply with data rules, using automation to track which rules are applicable as data is passed between processes and as derived data is generated. It encodes data-governance rules using a formal language and performs reasoning on multi-input-multi-output data-flow graphs in decentralised contexts. We test its power and utility by working with users performing cyclone tracking and earthquake modelling to support mitigation and emergency response. We query standard provenance traces to detach Dr.Aid from details of the tools and systems they are using, as these inevitably vary across members of a federation and through time. We evaluate the model in three aspects by encoding real-life data-use policies from diverse fields, showing its capability for real-world usage and its advantages compared with traditional frameworks. We argue that this approach will lead to more agile, more productive and more trustworthy collaborations and show that the approach can be adopted incrementally. This, in-turn, will allow more appropriate data policies to emerge opening up new forms of collaboration., Accepted for The 24th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW)

The emergence of the microservices-oriented architectural style calls for novel methodologies and technological frameworks that support the design, development, and main-tainance of applications structured according to this new style. In this paper, we consider the issue of designing suitable strategies for the governance and the automation of testing activities within the microservices paradigm. We focus on the problem of discovering relations between test programs that help avoiding to re-run all the available test suites each time one of its constituents evolves. We propose an analysis technique, based on symbolic execution of test programs, which is able to collect information about the invocations of local and remote APIs performed when running such programs. Symbolic execution enables the analysis of sets of executions corresponding to different input data, and hence it is also suitable for parametric test programs. The information extracted by symbolic execution is processed by a rule-based automated reasoning engine, which infers dependencies and similarities among test programs. In particular, test programs are considered similar if they involve the same microservice instance, or they connect to the same remote API, or they locally activate overlapping APIs, or they raise similar kinds of errors. We show the viability of our approach by presenting a case study within the context of a real-world microservice application that implements an open-source educational platform.

This paper offers a new practical approach toward automated commonsense reasoning with first-order logic (FOL) SUMO-based ontologies. We propose a new black-box evaluation framework for SUMO-based ontologies, which exploits the world knowledge encoded in WordNet and its mapping into SUMO. Our proposal consists of both a novel semi-automatic method for the creation of a large set of commonsense problems and a new procedure that enables its automatic evaluation by using automated theorem provers (ATPs). The application of our method enables the creation of a very large benchmark consisting of more than 15 000 problems from a small set of manually built question patterns that exploit the WordNet semantic relations. By means of the resulting benchmark, we successfully evaluate the competency of different translations of SUMO into FOL and the performance of various state-of-the-art FOL ATPs according to several quality criteria. A general analysis of our experimental results demonstrates that the proposed commonsense problems are heterogeneous and non-trivial. Furthermore, a fine-grained analysis of the experimental results obtained for a sample of our benchmark enables the detection of some mapping errors and some discrepancies between the knowledge of WordNet and SUMO. The evaluation benchmark and all the resources that have been used and developed during this work are released in a single package.

Cyber-Physical System (CPS) applications including human-robot interaction call for automated reasoning for rational decision-making. In the latter context, typically, audio-visual signals are employed. Τhis work considers brain signals for emotion recognition towards an effective human-robot interaction. An ElectroEncephaloGraphy (EEG) signal here is represented by an Intervals’ Number (IN). An IN-based, optimizable parametrickNearest Neighbor (kNN) classifier scheme for decision-making by fuzzy lattice reasoning (FLR) is proposed, where the conventional distance between two points is replaced by a fuzzy order function (σ) for reasoning-by-analogy. A main advantage of the employment of INs is that no ad hoc feature extraction is required since an IN may represent all-order data statistics, the latter are the features considered implicitly. Four different fuzzy order functions are employed in this work. Experimental results demonstrate comparably the good performance of the proposed techniques.

Radio-Frequency Identification (RFID) system technology is a key element for the realization of the Industry 4.0 vision, as it is vital for tasks such as entity tracking, identification and asset management. However, the plethora of RFID systems’ elements in combination with the wide range of factors that need to be taken under consideration along with the interrelations amongst them, make the problem of identification and design of the right RFID system, based on users’ needs particularly complex. The research outlined in this paper seeks to optimize this process by developing an integrating schema that will encapsulate this information in a form that is both human and machine processible. Human readability will allow a shared understanding of the RFID technology domain; machine readability, automated reasoning engines to perform logical deduction techniques returning implicit information. For this purpose, the novel RFID System Configuration Ontology (RFID SCO) is developed. Hence, non-RFID experts are enabled to identify the most suitable RFID system according to their needs and RFID experts to retrieve all the relevant information required for the efficient design of the corresponding RFID system. The RFID SCO is validated and tested successfully against real-world scenarios provided by domain experts.

Digital circuits are extensively used in computers and digital systems and it is of high importance to guarantee that these circuits are correct in order to prevent issues like the famous Pentium FDIV bug. Formal verification can be used to derive the correctness of a given circuit with respect to a certain specification. However arithmetic circuits, and most prominently multipliers, impose a challenge for existing verification techniques. Currently one of the most effective techniques is based on algebraic reasoning. In this approach the circuit is modeled as a set of pseudo-Boolean polynomials and the word-level specification is reduced by a Grobner basis, which is implied by the polynomial representation of the circuit. The circuit is correct if and only if the final result is zero. Nonetheless the verification process might not be error-free. Generating and automatically checking proofs independently increases confidence in the results of automated reasoning tools. In this paper we survey the current state of the art of this work. We give an overview over recent solving techniques, available benchmarks, and include a comprehensive evaluation.

Search-optimization problems are plentiful in scientific and engineering domains. MaxSAT and answer set programming with weak constraints (ASP-WC) are popular frameworks for modeling and solving search problems with optimization criteria. There is a solid understanding on how SAT relates to ASP. Yet, the question on how MaxSAT relates to ASP-WC is not trivial. The answer to this question provides us with the means for cross fertilization between distinct subareas of automated reasoning. In this paper, we propose a weighted abstract modular framework that allows us to (i) capture MaxSAT and ASP-WC and (ii) state the exact link between these distinct paradigms. These findings translate, for instance, into the immediate possibility of utilizing MaxSAT solvers for finding solutions to ASP-WC programs.

We have completed machine-assisted proofs of two highly-optimized cryptographic primitives, AES-256-GCM and SHA-384. We have verified that the implementations of these primitives, written in a mix of C and x86 assembly, are memory safe and functionally correct, by which we mean input-output equivalent to their algorithmic specifications. Our proofs were completed using SAW, a bounded cryptographic verification tool which we have extended to handle embedded x86. The code we have verified comes from AWS LibCrypto. This code is identical to BoringSSL and very similar to OpenSSL, from which it ultimately derives. We believe we are the first to formally verify these implementations, which protect the security of nearly everybody on the internet.

Model-based software engineering plays an increasing role in system development. The abstractions offered by models provide a basis for tasks such as analysis, synthesis, and automated reasoning. However, like traditional software engineering, model-based engineering must also deal with challenges that arise during system evolution, including requirement changes and platform updates. This paper describes our vision for a model-based workflow for adaptive software that reduces the burden caused by evolution. Our vision includes a modeling paradigm centered around the concepts of objectives, intents, and constraints, which define, respectively, (1) what the system must do in terms of domain-specific abstractions, (2) the concretization choices made to refine a model into implementation, and (3) the system requirements not expressed in terms of domain-specific abstractions. We also discuss a vision of integrated program synthesis via refinement in a theorem prover.

Premise selection is a fundamental task for automated reasoning in large theories. A recently proposed approach formulates premise selection as a sequence-to-sequence problem, called stateful premise selection. Given a theorem statement, the goal of a stateful premise selection method is to predict the set of premises that would be useful in proving it. In this work we use the Transformer architecture for learning the stateful premise selection method. We outperform the existing recurrent neural network baseline and improve upon the state of the art on a recently proposed dataset.

The GDPR is one of many legal texts which can greatly benefit from the support of automated reasoning. Since its introduction, efforts were made to formalize it in order to support various automated operations. Nevertheless, widespread and efficient automated reasoning over the GDPR has not yet been achieved. In this paper, a tool called the NAI suite is being used in order to annotate article 13 of the GDPR. The annotation results in a fully formalized version of the article, which deals with the transparency requirements of data collection and processing. Automated compliance checking is then being demonstrated via several simple queries. By using the public API of the NAI suite, arbitrary tools can use this procedure to support trust management and GDPR compliance functions.

The identification of network attacks in real-time is becoming increasingly important. Most Artificial Intelligence (AI) applications use machine learning to do the classification of attack types but the advantage of an ontological approach is that automated reasoning is the underpinning theory rather than automated learning. Automated reasoners allow automated classification and this powerful feature is the basis for the developing of an early warning system for active network attacks. In this paper, the authors describe how to employ Semantic Technologies by building an ontology to identify network attack types in order to support the automated classification of current network attacks by recognising relevant properties which are then mapped to relevant attack scenarios depicted in the ontology. The classes and relationships of the ontology are described formally and implemented in Protege, an ontology editor. The Attack Scenario class, a core class of the ontology, represents types of network attacks, for example, a Denial of Service attack. The ontology is evaluated by showing three examples of real attacks that are correctly classified by the presented ontology.

Over the recent years deep learning has found successful applications in mathematical reasoning. Today, we can predict fine-grained proof steps, relevant premises, and even useful conjectures using neural networks. This extended abstract summarizes recent developments of machine learning in mathematical reasoning and the vision of the N2Formal group at Google Research to create an automatic mathematician. The second part discusses the key challenges on the road ahead.

Automated reasoning support is an important aspect of logic-based knowledge representation. The development of specialised procedures and sophisticated optimisation techniques significantly improved the performance even for complex reasoning tasks such as conjunctive query answering. Reasoning and query answering over knowledge bases with a large number of facts and expressive schemata remains, however, challenging.

Smart city services are typically defined according to domains (e.g., health, education, safety) and supported by different systems. Consequently, the analysis of smart city data is often domain-specific, thus limiting the capabilities of the offered services and hampering decision-making that relies on isolated domain information. To support a suitable analysis across multiple domains, it is necessary having a unified data model able to handle the inherent heterogeneity of smart city data and take into account both geographic and citizen information. This paper presents an ontology-based information model to support multi-domain analysis in smart cities to foster interoperability and powerful automated reasoning upon unambiguous information. The proposed information model follows Linked Data principles and takes advantage of ontologies to define information semantically. The semantic relationships and properties defined in the model also allow inferring new pieces of information that improve accuracy when analyzing multiple city domains. This paper reports an evaluation of the information model through ontological metrics and competence questions.

Current advancements in learning technology necessitate the development of personalized software for the students, respecting their cognitive states. In the light of these novel approaches in digital education, this paper presents a framework for the automated reasoning of the students’ cognitive states and function. The cognitive states are depicted using the Revised Bloom Taxonomy, representing a continuum of increasing cognitive complexity. Moreover, the automatic reasoning is achieved using classification analysis and more specifically the k-Nearest Neighbors algorithm, which takes as input personal and cognitive students’ characteristics. As such, each new student is classified in the appropriate level of the taxonomy. To this end, a more personalized learning experience is offered to them and a student-centered learning environment in created based on the distinct pace of instruction that each student wants to receive. The presented approach has been incorporated in a learning technology system for teaching computer programming and was evaluated with promising results.

Executable Domain-Specific Languages (DSLs) are a promising paradigm in software systems development because they are aiming at performing early analysis of a system’s behavior. They can be simulated and debugged by existing Model-Driven Engineering (MDE) tools leading to a better understanding of the system before its implementation. However, as the quality of the resulting system is closely related to the quality of the DSL, there is a need to ensure the correctness of the DSL and apply execution engines with a high level of trust. To this aim we developed Meeduse, a tool in which the MDE paradigm is mixed with a formal method assisted by automated reasoning tools such as provers and model-checkers. Meeduse assists the formal definition of the DSL static semantics by translating its meta-model into an equivalent formal B specification. The dynamic semantics can be defined using proved B operations that guarantee the correctness of the DSL’s behavior with respect to its safety invariant properties. Regarding execution, Meeduse applies the ProB animator in order to animate underlying domain-specific scenarios.

Software configurability opens the door to misconfiguration vulnerabilities, invalid settings that expose software weaknesses. Misconfiguration is one the top ten most critical security risks and the most common. This paper envisions a world without misconfiguration vulnerabilities through the use of automated reasoning techniques to infer and secure software configurations. Real-world software, however, often lacks an explicit specification of secure configurations, relying on hand-validation by users. Real-world systems comprise many individual highly-configurable software components, making the space of possible configurations for the whole system enormous. To realize our vision and overcome these challenges, we aim to create a rigorous definition of configuration specifications, use formal methods to mechanize the inference and generation of valid configurations, and develop algorithms to automatically secure against misconfiguration.

Artificial Intelligence (AI) is a growing field of computa- tional science techniques designed to mimic functions per- formed by people. Advancements in autonomy will depend on a portfolio of AI technologies. Automated planning and scheduling is a venerable field of study in AI, and is needed for a variety of mission planning functions. Plan execution technology is less well studied, but important for auton- omy and robotics. Specialized forms of automated reason- ing and machine learning are key technologies to enable fault management. Over the past decade, the NASA Au- tonomous Systems and Operations (ASO) project has devel- oped and demonstrated numerous autonomy enabling tech- nologies employing AI techniques. Our work has employed AI in three distinct ways to enable autonomous mission op- erations capabilities. Crew Autonomy gives astronauts tools to assist in the performance of each of these mission oper-ations functions. Vehicle System Management uses AI tech- niques to turn the astronaut's spacecraft into a robot, allow- ing it to operate when astronauts are not present, or to reduce astronaut workload. AI technology also enables Autonomous Robots as crew assistants or proxies when the crew are not present. When these capabilities are used to enable astro- nauts to operate autonomously, they must be integrated with user interfaces, introducing numerous human factors con- siderations; when these capabilities are used to enable vehi- cle system management, they must be integrated with flight software, and run on embedded processors under the control of real-time operating systems.We first describe human spaceflight mission operations capabilities. The remainder of the paper will describe the ASO project, and the development and demonstration per- formed by ASO since 2011. We will describe the AI tech- niques behind each of these demonstrations, which include a variety of symbolic automated reasoning and machine learn- ing based approaches. Finally, we conclude with an assess- ment of future development needs for AI to enable NASA's future Exploration missions.

Artificial intelligence (AI) is currently a much discussed topic evoking many expectations and fears. For a realistic assessment of the opportunities and risks of AI, basic understanding of what makes AI applications different from other computer programs is necessary. How and what AI concepts can be introduced in primary and secondary education depends on the age and the computer science background of the students. In the talk, a sample of topics and methods for introducing AI in school is presented - covering machine learning as well as automated reasoning, offering unplugged material for students without background in computer science as well as possibilities for more formal introduction of AI algorithms and AI programming. In the second part of the talk, learning analytics and intelligent tutor systems are introduced as applications of AI methods to support students and teachers. While learning analytics is mainly behavioristic with focus on applying machine learning to prediction of students' performance, intelligent tutor systems are based on cognitive and constructivist principles with focus on AI methods for individualized diagnosis of misconceptions and feedback generation.

We present a visual and interactive taxonomic Artificial Intelligence (AI) tool, the Automated Taxonomic Concept Reasoner (ATCR), whose graphical web interface is under development and will also become available via an Application Programming Interface (API). The tool employs automated reasoning (Beeson 2014) to align multiple taxonomies visually, in a web browser, using user or expert-provided taxonomic articulations, i.e. "Region Connection Calculus (RCC-5) relationships between taxonomic concepts, provided in a specific logical language (Fig. 1). It does this by representing the problem of taxonomic alignment under these constraints in terms of logical inference, while performing these inferences computationally and leveraging the powerful Microsoft Z3 Satisfiability Modulo Theory (SMT) solver (de Moura and Bjørner 2008). This tool represents further development of utilities for the taxonomic concept approach, which fundamentally addresses the challenge of robust biodiversity data aggregation in light of multiple conflicting sources (and source classifications) from which primary biodiversity data almost invariably originate. The approach has proven superior to aggregation, based just on the syntax and semantics provided by the Darwin Core standard Franz and Sterner 2018). Fig. 1 provides an artificial example of such an alignment. Two taxonomies, A and B, are shown. There are five taxonomic concepts, A.One, A.Two, A.Three, B.One and B.Two. A.Two and A.Three are sub-concepts (children) of A.One, and B.Two is a sub-concept (child) of B.One. These are represented by the direction of the grey arrows. The undirected mustard-coloured lines represent relationships, i.e., the articulations referred to in the previous paragraph. These may be of five kinds: congruent (==), includes (), overlap (> "sibling" concepts are disjoint in their instances, all instances of a parent concept are instances of at least one of its child concepts, and every concept has at least one instance - the SMT-based automated reasoner is able to deduce the relationships represented by the undirected green lines. It is also able to deduce disjunctive relationships where these are logically implied. "sibling" concepts are disjoint in their instances, all instances of a parent concept are instances of at least one of its child concepts, and every concept has at least one instance - the SMT-based automated reasoner is able to deduce the relationships represented by the undirected green lines. It is also able to deduce disjunctive relationships where these are logically implied. ATCR is related to Euler/X (Franz et al. 2015), an existing tool for the same kinds of taxonomic alignment problems, which was used, for example, to obtain an alignment of two influential primate classifications (Franz et al. 2016). It differs from Euler/X in that it employs a different logical encoding that enables more efficient and more informative computational reasoning, and also in that it provides a graphical web interface, which Euler/X does not.

The development of safety-critical systems requires the control of hazards that can potentially cause harm. To this end, safety engineers rely during the development phase on architectural solutions, called safety patterns, such as safety monitors, voters, and watchdogs. The goal of these patterns is to control (identified) faults that can trigger hazards. Safety patterns can control such faults by e.g., increasing the redundancy of the system. Currently, the reasoning of which pattern to use at which part of the target system to control which hazard is documented mostly in textual form or by means of models, such as GSN-models, with limited support for automation. This paper proposes the use of logic programming engines for the automated reasoning about system safety. We propose a domain-specific language for embedded system safety and specify as disjunctive logic programs reasoning principles used by safety engineers to deploy safety patterns, e.g., when to use safety monitors, or watchdogs. Our machinery enables two types of automated safety reasoning: (1) identification of which hazards can be controlled and which ones cannot be controlled by the existing safety patterns; and (2) automated recommendation of which patterns could be used at which place of the system to control potential hazards. Finally, we apply our machinery to two examples taken from the automotive domain: an adaptive cruise control system and a battery management system., In Proceedings ICLP 2020, arXiv:2009.09158

With the fourth industrial revolution, the OPC Unified Architecture (OPC UA) [1] has emerged as the standard communication framework for the implementation of cyber-physical production systems since it can be used for both, communication and information modeling. Even though OPC UA helps bridge the interoperability gap at the automation level, its semantic has not yet been formally defined and generated or manually created OPC UA information models are often incomplete and inconsistent making an efficient automated reasoning and knowledge inference on the OPC UA address space particularly challenging.In this paper, we show that it is possible to train a machine learning system on OPC UA information models, such that it performs automated reasoning over OPC UA knowledge graphs with high precision and recall. More specifically, we present a reinforcement learning based solution that learns to reason on semantically incomplete OPC UA information models by constructing multi-hop relational paths along an embedded vector space of the knowledge graph representing the information model. The construction of such relational paths allows the discovery of missing relations between the entities and at the same time the evaluation of the truth of the encoded triples, thus enabling consistency checks and questions answering.

The benefits of automatic identification technologies in healthcare have been largely recognized. Nevertheless, unlocking their potential to support the most knowledge-intensive medical tasks requires to go beyond mere item identification. This paper presents an innovative Decision Support System (DSS), based on a semantic enhancement of Near Field Communication (NFC) standard. Annotated descriptions of medications and patient&rsquo, s case history are stored in NFC transponders and used to help caregivers providing the right therapy. The proposed framework includes a lightweight reasoning engine to infer possible incompatibilities in treatment, suggesting substitute therapies. A working prototype is presented in a rheumatology case study and preliminary performance tests are reported. The approach is independent from back-end infrastructures. The proposed DSS framework is validated in a limited but realistic case study, and performance evaluation of the prototype supports its practical feasibility. Automated reasoning on knowledge fragments extracted via NFC enables effective decision support not only in hospital centers, but also in pervasive IoT-based healthcare contexts such as first aid, ambulance transport, rehabilitation facilities and home care.

Complex societal problems require a multidisciplinary and multi-method approach to develop models that can support the development of solutions. General morphological analysis is a qualitative method to extract information from experts through facilitation and the use of customized software. Ontologies provide semantic representation of knowledge bases together with automated reasoning capabilities. These two approaches, combined with the use of concept maps, provide an integrated approach which can be used to understand complex and ill-structured problem domains and to aid in business modelling, strategy and scenario development and finally, decision-making. The resulting models are subjective constructs reflecting the knowledge and understanding of the analysts. Subsequent synthesis of new understanding and decisions rely on the robust validation and verification of the underlying logic and assumptions of the conceptual models. Morphological Analysis and ontological constructs are applied in terms of an integrated Morphological Ontology Design Engineering methodology (MODE), which is based on Design Science. The paper is developed around the opportunity of scoping the applied research competence required to support a nation's progress toward energy sufficiency. This paper presents a complex fused model for national energy sufficiency in New Zealand. The approach can be used to address other ill-structured complex societal problems.

Medical practitioners record the condition status of a patient through qualitative and quantitative observations. The measurement of vital signs and molecular parameters in the clinics gives a complementary description of abnormal phenotypes associated with the progression of a disease. The Clinical Measurement Ontology (CMO) is used to standardize annotations of these measurable traits. However, researchers have no way to describe how these quantitative traits relate to phenotype concepts in a machine-readable manner. Using the WHO clinical case report form standard for the COVID-19 pandemic, we modeled quantitative traits and developed OWL axioms to formally relate clinical measurement terms with anatomical, biomolecular entities and phenotypes annotated with the Uber-anatomy ontology (Uberon), Chemical Entities of Biological Interest (ChEBI) and the Phenotype and Trait Ontology (PATO) biomedical ontologies. The formal description of these relations allows interoperability between clinical and biological descriptions, and facilitates automated reasoning for analysis of patterns over quantitative and qualitative biomedical observations.

With the continuous progress of science and technology, the mechanical field is also constantly upgrading from traditional mechanical engineering to the mechatronics engineering and artificial intelligence (AI) is one of them. AI deals with a computer program that possesses own decision-making capability to solve a problem of interest with imitates the intelligent behavior of expertise which finally turns into higher productivity with better quality output. From the inception, various developments have been done on AI system which nowadays widely implemented in the mechanical and/or manufacturing industries with broaden area of application such as pattern recognition, automation, computer vision, virtual reality, diagnosis, image processing, nonlinear control, robotics, automated reasoning, data mining and process control systems. In this study, review attempt has been made for AI technologies used in various mechanical fields such as thermal, manufacturing, design, quality control and various connected fields of mechanical engineering. The study shows the blend mixed of AI technologies like deep convolutional neural network (DCNN), convolutional neural network (CNN), artificial neural network (ANN), fuzzy logic and many more to control the process parameters, process planning, machining, quality control and optimization in the mechanical era for smooth development of product or system. With the implementation of AI in mechanical engineering applications, the error, rejection of components can be minimized or eliminated and system optimization can be achieved effectively turn in economical better quality products.

This paper demonstrates that constrained pseudorandom number can solve classic coin-weighing puzzles faster than human-devised algorithms. Deep learning and ensemble machine learning lie in inductive methods while automated reasoning implemented in deductive computer languages is based on deductive methods. In the inductive methods, intelligence is inferred by pseudorandom number for creating the sophisticated decision trees. In machine learning, the goal is to learn a function that minimizes an error or one that maximizes reward over punishment. This paper shows a new challenge that every solution candidate is solely generated by the constrained pseudorandom number, while a simple deductive rule is applied for selecting solutions among the generated candidates. In this paper, the computation performance of the proposed method was measured by comparing with the existing open source codes by solving classic 12-coin and 24-coin puzzles, respectively. The proposed method can be used for solving quantitative group testing in biology and medicine.

International audience

Cyber-physical systems are ubiquitous nowadays. However, as automation increases, modeling and verifying them becomes increasingly difficult due to the inherently complex physical environment. Skill graphs are a means to model complex cyber-physical systems (e.g., vehicle automation systems) by distributing complex behaviors among skills with interfaces between them. We identified that skill graphs have a high potential to be amenable to scalable verification approaches in the early software development process. In this work, we suggest combining skill graphs with hybrid programs. Hybrid programs constitute a program notation for hybrid systems enabling the verification of cyber-physical systems. We provide the first formalization of skill graphs including a notion of compositionality and propose Skeditor, an integrated framework for modeling and verifying them. Skeditor is coupled with the theorem prover KeYmaera X, which is specialized in the verification of hybrid programs. In an experiment exhibiting the follow mode of a vehicle, we evaluate our skill-based methodology with respect to savings in verification effort and potential to find modeling defects at design time. Compared to non-compositional verification, the initial verification effort needed is reduced by more than 53%.

Inconsistencies in legal texts are sometimes hard to find and correct. In this paper a novel machine executable methodology is described, which can facilitate this process. We demonstrate the methodology on the American University of Paris Library Circulation Policy No. AA036EN.

Reasoning about gene networks is essential from various perspectives, such as predicting side effects of drugs or explaining unusual cellular behavior. Because of the massive size of these gene networks, a biologist can only work on a small part of the network. Thus, there is an essential requirement for logical representations and automated reasoning on such networks to help biologists to understand genetic interactions. However, the knowledge about gene networks is always incomplete and sometimes not accurate. Hence, knowledge has to be continuously revised and extended. In this work, we propose an approach based on non-monotonic logic programming, and the framework of Answer Set Programming(ASP), to represent and handle gene networks. We show how to model reasoning, predict events, and explain observations in gene networks. Finally, we show how our approach is applied to represent and resolve the DNA double-strand breaks, which is one of the most severe genomic lesions.

The article reviews the functional and structural organization of the Multi-Agent System of Knowledge Representation and Processing (MASKRP). The architecture of the MASKRP, the models of reactive and cognitive software agents are described. The examples of the interaction, states and transitions diagrams of the software agents, the systems of rules and the queries of the problem-oriented multi-agent solver are given. A classical logic solver and the multi-agent solver, which is developed on the basis of the software agents models described in this article, are compared. The results of fuzzy queries to the knowledge base, which were realized according to the sets of fuzzy rules and membership functions specified in the example, are shown. The results demonstrate the practical viability and efficiency of the presented approach to implementation of a multi-agent system based on fuzzy knowledge. At the heart of design decisions at creation of the MASKRP the requirement of support of users of different competence level is put. To support the process of knowledge acquisition and user’s comfort setting up mechanisms of automated reasoning developed the problem-oriented toolkit of visual design.

Various state-of-the-art automated reasoning (AR) tools are widely used as backend tools in research of knowledge representation and reasoning as well as in industrial applications. In testing and verification, those tools often run continuously or nightly. In this work, we present an approach to reduce the runtime of AR tools by 10% on average and up to 20% for long running tasks. Our improvement addresses the high memory usage that comes with the data structures used in AR tools, which are based on conflict driven no-good learning. We establish a general way to enable faster memory access by using the memory cache line of modern hardware more effectively. Therefore, we extend the standard C library (glibc) by dynamically allowing to use a memory management feature called huge pages. Huge pages allow to reduce the overhead that is required to translate memory addresses between the virtual memory of the operating system and the physical memory of the hardware. In that way, we can reduce runtime, which in turn decreases costs of running AR tools and applications with similar memory access patterns by linking the tool against this new glibc library when compiling it. In every day industrial applications, runtime savings allow to include more detailed verification tasks, getting better results of any-time optimization algorithms with a bound execution time, and save energy during nightly software builds. To back up the claimed speed-up, we present experimental results for tools that are commonly used in the AR community, including the domains ASP, hardware and software BMC, MaxSAT, and SAT.

Artificial Intelligence is one of the fastest growing disciplines, disrupting many sectors. Originally mainly for computer scientists and engineers, it has been expanding its horizons and empowering many other disciplines contributing to the development of many novel applications in many sectors. These include medicine and health care, business and finance, psychology and neuroscience, physics and biology to mention a few. However, one of the disciplines in which artificial intelligence has not been fully explored and exploited yet is education. In this discipline, many research methods are employed by scholars, lecturers and practitioners to investigate the impact of different instructional approaches on learning and to understand the ways skills and knowledge are acquired by learners. One of these is qualitative research, a scientific method grounded in observations that manipulates and analyses non-numerical data. It focuses on seeking answers to why and how a particular observed phenomenon occurs rather than on its occurrences. This study aims to explore and discuss the impact of artificial intelligence on qualitative research methods. In particular, it focuses on how artificial intelligence have empowered qualitative research methods so far, and how it can be used in education for enhancing teaching and learning.

An autoformalization system is an AI that learns to read natural language content and to turn it into an abstract, machine verifiable formalization, ideally by bootstrapping from unlabeled training data with minimum human interaction. This is a difficult task in general, one that would require strong automated reasoning and automated natural language processing capabilities. In this paper, it is argued that autoformalization is a promising path for systems to learn sophisticated, general purpose reasoning in all domains of mathematics and computer science. This could have far reaching implications not just for mathematical research, but also for software synthesis. Here I provide the outline for a realistic path towards those goals and give a survey of recent results that support the feasibility of this direction.

An important aspect in system of systems (SoS) is the realization of the capabilities in different systems that work together. Identifying and locating these capabilities are important to orchestrate the overall activities and hereby to achieve the overall goal of the SoS. System elements and capabilities in SoS however, are rarely stable and need to evolve in different ways and different times in accordance with the changing requirements. To manage the SoS and cope with its evolution it is necessary that the dependency links to the capabilities and the system elements can be easily traced. Several approaches have been proposed to model traceability and reason about these by extending a predetermined set of possible trace links with fixed semantics. However, for the context of SoS a fixed traceability model with fixed traceability semantics is limited to consider the various different and changing scenarios. In this article, we first present the different traceability requirements for managing traceability in the context of SoS. Subsequently, we present the metamodel and the corresponding domain specific language to support modeling traceability and traceability analysis approaches within the evolving SoS context. Further, we provide the tool support for automated reasoning of traceability of SoS capabilities and system elements. We illustrate and discuss the approach for the application to a smart city SoS.