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Computing is moving towards pervasive, ubiquitous environments in which devices, software agents and services are all expected to seamlessly integrate and cooperate in support of human objectives. An important next step for pervasive computing is the integration of intelligent agents that employ knowledge and reasoning to understand the local context and share this information in support of intelligent applications and interfaces. Such scenarios, characterised by "computation everywhere around us", require on the one hand software components with intelligent behaviour in terms of objectives and context, and on the other their integration so as to produce social intelligence. Logic Programming (LP) has been recognised as a natural paradigm for addressing the needs of distributed intelligence. Yet, the development of novel architectures, in particular in the context Internet of Things (IoT), and the emergence of new domains and potential applications, are creating new research opportunities where LP could be exploited, when suitably coupled with agent technologies and methods so that it can fully develop its potential in the new context. In particular, the LP and its extensions can act as micro-intelligence sources for the IoT world, both at the individual and the social level, provided that they are reconsidered in a renewed architectural vision. Such micro-intelligence sources could deal with the local knowledge of the devices taking into account the domain specificity of each environment. The goal of this thesis is to re-contextualise LP and its extensions in these new domains as a source of micro-intelligence for the IoT world, envisioning a large number of small computational units distributed and situated in the environment, thus promoting the local exploitation of symbolic languages with inference capabilities. The topic is explored in depth and the effectiveness of novel LP models and architectures -and of the corresponding technology- expressing the concept of micro-intelligence is tested.

This work provides a formal model for the burden of persuasion in legal proceedings. The model shows how the allocation of the burden of persuasion may induce a satisfactory outcome in contexts in which the assessment of conflicting arguments would, without such an allocation, remain undecided. The proposed model is based on an argumentation setting in which arguments may be accepted or rejected according to whether the burden of persuasion falls on the conclusion of such arguments or on its complements. Our model merges two ideas that have emerged in the debate on the burden of persuasion: the idea that allocation of the burden of persuasion makes it possible to resolve conflicts between arguments, and the idea that its satisfaction depends on the dialectical statuses of the arguments involved. Our model also addresses cases in which the burden of persuasion is inverted, and cases in which burdens of persuasion are inferred through arguments.

The Intelligent Edge computing paradigm is playing a major role in the design and development of Cyber-Physical and Cloud Systems (CPCS), extending the Cloud and overcoming its limitations so as to better address the issues related with the physical dimension of data—and therefore of the data-aware intelligence (such as context-awareness and real-time responses). Despite the proliferation of research works in this area, a well-founded software engineering approach specifically addressing the distribution of intelligence sources between the Edge and the Cloud is still missing. In this paper we propose some general criteria along with a coherent set of guidelines to follow in the design of distributed intelligence within CPCS, suitably exploiting Edge and Cloud paradigms to effectively enable data intelligence and accounting for both symbolic and sub-symbolic approaches to reasoning. Then, we exploit the notion of micro-intelligence as situated intelligence for Edge computing, promoting the idea of intelligent environment embodying rational processes meant to complement the cognitive process of individuals in order to reduce their cognitive workload and augment their cognitive capabilities. In order to demonstrate the general applicability of our guidelines, we propose Situated Logic Programming (SLP) as the conceptual framework for delivering micro-intelligence in CPCS, and Logic Programming as a Service (LPaaS) as its reference architecture and technological embodiment.

Distributing situated intelligence in Cyber-Physical Systems (CPS) to realise the vision of Internet of Intelligent Things (IoIT) raises issues of efficiency and scalability - in particular when dealing with huge numbers of physical objects. Such issues do not just regard the application or service logic and runtime, but also impact on the software development process. Moving from the notion of Logic Programming as a Service (LPaaS) - a re-interpretation of distributed logic programming tailored to the IoT era - in this paper we describe how its architecture and development process deals with the aforementioned issues from a software engineering standpoint, by discussing the design, development practices, and delivery means of the LPaaS technology.

New generations of distributed systems are opening novel perspectives for logic programming (LP): On the one hand, service-oriented architectures represent nowadays the standard approach for distributed systems engineering; on the other hand, pervasive systems mandate for situated intelligence. In this paper, we introduce the notion ofLogic Programming as a Service(LPaaS) as a means to address the needs of pervasive intelligent systems through logic engines exploited as a distributed service. First, we define the abstract architectural model by re-interpreting classical LP notions in the new context; then we elaborate on the nature of LP interpreted as a service by describing the basic LPaaS interface. Finally, we show how LPaaS works in practice by discussing its implementation in terms of distributed tuProlog engines, accounting for basic issues such as interoperability and configurability.

The testing of the performance of opportunistic communication protocols and applications is usually done through simulation as i) deployments are expensive and should be left to the final stage of the development process, and ii) the number of varying parameters in thesesystems is so high that it would be very hard to conduct thorough testing of all the functionality within a single deployment. Therefore, protocols and applications are often plugged into mobility simulators to test their performance; however, until recently, most of the testing has been conducted with random mobility models which do not mirror reality. Furthermore, despite disconnections playing a veryprominent role in the performance of any opportunistic mobile system, most models do not really account for it. A different approach to testing is the use of real traces of movement collected in specific domains as test cases. These cases, however, do not allow for flexible performance testing, as they are specific for a given scenario withfixed connectivity properties. In this paper we propose the Connectivity Trace Generator (CTG), a tool for the automatic generation of connectivity traces, which takes as input real mobility traces and is able to output a set of traces with similar connectivity properties, which can be used as test cases. This allows developers to investigate the impact of the variation of connectivity patterns, number of hosts, and other parameters on the protocol or application under investigation. We use a real case study (the Dartmouth campus connectivity traces) to show how CTG allows protocol developers to play with some connectivity and density parameters so to best conduct performance testing of different aspects of protocols and applications. Copyright 2007 ACM.

The blockchain is a novel approach to support distributed systems enabling a common, consistent view of a shared state among distributed nodes. There, smart contracts are computer programs that allow users to deploy arbitrary computations, in charge of automatically regulate state transitions and enforce properties. In this paper we speculate on how the blockchain and smart contracts could take advantage of a logic programming approach, and, complementarily, on how logic programming can benefit from the blockchain infrastructure. Accordingly, we discuss some possible research directions and open questions for future research.

Situatedness is a fundamental requirement for today’s complex software systems, as well as for the computational models and programming languages used to build them. Spatial and temporal situatedness, in particular, are essential features for AI, enabling actors of the system to take autonomous decisions contextual to the space-time they live in. To support spatio-temporal awareness in distributed pervasive systems, we adopt the standpoint of Logic Programming (LP) by focussing on the Logic Programming as a Service (LPaaS) approach, promoting the distribution of situated intelligence. Accordingly, we provide an interpretation about what it means to make LP span across space and time, then we extend the LPaaS model and architecture towards spatio-temporal situatedness, by identifying a set of suitably-expressive spatio-temporal primitives.

The original s(CASP) implementation is a stand-alone program implemented in Ciao Prolog. It reads the s(CASP) source using a dedicated parser, resolves the query embedded in the source and emits the results in a format dictated by commandline options. Typical applications require composing a s(CASP) program, solving a query and reasoning about the bindings, model and/or justification. This is often done in some external language, e.g., Python. In this paper we propose a closer integration with Prolog. The s(CASP) program is simply a Prolog program that has to respect certain constraints. The scasp library can be used to solve a query using s(CASP) semantics, making the bindings available in the normal Prolog way and providing access to the model and justification as Prolog terms. This way, we can exploits Prolog’s power for manipulating (Prolog) terms for construction the s(CASP) program and interpreting the results.