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Four-valued semantics is applied to a deduction basis for granular reasoning with zooming operation. This enables the granular reasoning useful for the foundation of reasoning for incomplete and inconsistent information. We interpret the semantics of zooming operation with four-valued semantics based on partial interpretation and construct a deductive system with Belnap's four-valued logic. We also discuss an application with non-monotonic reasoning and paraconsistent logic using the zooming technique for frame problem.

Combinatorial testing has proven to be a very valuable testing technique for automated generation of test suites given the domain of the inputs and the configuration parameters. In order to fully automate combinatorial testing, however, there is a need for an automated test oracle. In case of testing logic-based non-monotonic reasoning systems, we show how to generate test cases and how to make use of metamorphic testing in order to provide a test oracle. The combined testing method allows for complete test automation. We further discuss first experimental results obtained for an implementation of an assumption-based truth maintenance system implementing basic non-monotonic reasoning capabilities.

Currently knowledge graph has been widely applied to various fields. Although the data scale is large, there is still a lot of useful but implicit information in it. Thus, a powerful reasoning system is required to derive these data. However, current reasoning systems cannot accomplish this task very well. On the one hand, stand-alone reasoning systems cannot meet the demand of large data. On the other hand, the reasoning ability of existing distributed reasoning systems is limited because of the lack of expressive inference rules. In this paper, we propose and implement a distributed reasoning system KGRL for knowledge graph based on OWL2 RL. It has a more powerful reasoning ability due to more expressive rules. It also supports optimization for redundant data. Besides, a rule-based algorithm is designed to find the inconsistent data. Experimental results show that KGRL can derive more implicit information efficiently compared to other reasoning systems. Moreover, KGRL is capable of eliminating redundant data, which can reduce the storage of knowledge graph by an average of 42%. Finally, KGRL also performs well for the detection of inconsistencies in knowledge graph.

We present a new approach with respect to spatial reasoning problems by using logic programs. Because the weak completion of a logic program admits a least model under the three-valued ∊ukasiewicz semantics and this semantics has been successfully applied to other human reasoning tasks, conditionals are evaluated under these least ∊-models. We show that the weak completion semantics can also handle spatial relations in a way humans do. In particular, we develop a computational logic approach to spatial reasoning and show that the weak completion semantics computes preferred mental models.

Abductive inference is a form of backwards logical reasoning that infers likely hypotheses from a given conclusion. In other words, given an invalid implication of the form A => B, abduction asks the question "What formula C do we need to conjoin with the antecedent A so that (i) A & C => B is logically valid and (ii) C is consistent with A?" Abductive reasoning has found many applications in program verification and synthesis, particularly in modular program analysis, invariant generation, and automated inference of missing program expressions. This tutorial will give an overview of logical abduction and algorithms for performing abductive inference. We will also survey several use cases of abductive inference in the context of program analysis, verification, and synthesis.

Belief revision is a basic approach to deal with the contradiction in non-monotonic reasoning. It achieves the consistency of the belief base by removing members from it, which causes information loss unavoidably. Compared with belief revision, non-revision reasoning develops new reasoning methods to deal with the contradiction instead of modifying the initial base. This paper is an extending work of non-revision reasoning with beliefs in predicate logic. It's assumed that belief is represented by clause and assigned degrees of preference to it. An unique, consistent, deductively closed and most preferred extension of the belief base is constituted in this paper. Compared with the recent work, this approach is more efficient because it removes the redundancy of the system. More properties of the approach are proved in this paper. Especially, the epistemic process of this reasoning method is convergent.

Non-revision reasoning is a process to derive consistent belief set from an inconsistent belief base. Compared with belief revision, it doesn't modify the initial belief base, but developing new reasoning methods to deal with the contradiction. In this paper, an approach of non-revision reasoning in predicate logic is provided. We assume that belief is represented by clause and assign degrees of preference to it. It is possible to define a unique, consistent and deductively closed extension which is most preferred and derivable from the belief base. We argue that this approach can get a larger extension set losing less information than the present non-revision method. Finally, a threshold is used to reduce the complexity of belief base when there is not enough time to compute completely.

Annotated logics were proposed as foundations for Para consistent logic programming, and later studied as a class of non-classical logics. We propose evidential annotated logics Et for evidential reasoning which is based on beliefs and disbeliefs. We give a formal presentation of Et and introduce uncertainty and certainty degrees for practical application.

Temporal Equilibrium Logic (TEL) (Cabalar" a Vega, 2007) is a promising framework that extends the knowledge representation and reasoning capabilities of Answer Set Programming with temporal operators in the style of LTL. To our knowledge it is the first nonmonotonic logic that accommodates fully the syntax of a standard temporal logic (specifically LTL) without requiring further constructions. This paper provides a systematic complexity analysis for TEL for the (consistency) problem of checking the existence of a temporal equilibrium model of a TEL formula. It was previously shown that this problem in the general case lies somewhere between PSPACE and EXPSPACE. Here we establish a lower bound matching the EXPSPACE upper bound in (Cabalar" a Demri, 2011). Additionally we analyse the complexity for various natural subclasses of TEL formulas, identifying both tractable and intractable fragments. Finally the paper offers some new insights on the logic LTL by addressing satisfiability for minimal LTL models. The complexity results obtained highlight a substantial difference between interpreting LTL over finite or infinite words.

The past mineral deposit prediction expert systems cannot support nonmonotonic reasoning. This article introduces answer set programming, which is a well-known formalism in artificial intelligence, to design an expert system for mineral deposit prediction. The system includes GIS spatial database, fact generator, temporary fact library, fact management module, knowledge based management module, knowledge base, reasoning machine, and explanation module. The incomplete deposit knowledge and nonmonotonic reasoning is supported in the system. To demonstrate the feasibility of our approach, we illustrate how to represent deposit information in the form of logic programs and how to make reasoning according to the answer set semantics by an example of the prospecting model of porphyrite iron. The processing of ore deposit predicting is a typical example of nonmonotonic reasoning. As we got more information, we found that the original conclusion was wrong, then we revised our conclusion. Such behavior is supported in our system but is not supported in previous similar systems.

Enabling software systems to adjust their protection in continuously changing environments with imperfect context information is a grand challenging problem. The issue of uncertain reasoning based on imperfect information has been overlooked in traditional logic programming with classical negation when applied to dynamic systems. This paper sketches a non-monotonic approach based on Answer Set Programming to reason with imperfect context data in adaptive security where there is little or no knowledge about certainty of the actions and events.

Knowledge formalization and reasoning automation are central within Artificial Intelligence. Classical logic has been traditionally used for such purposes. However, it is better suited to deal with complete knowledge in ideal circumstances. In real situations, in which the knowledge is partial, classical logic is not sufficient since it is monotonic. No monotonic logics have been proposed to better cope with practical reasoning. A successful formalization of no monotonic reasoning is Reiter's default logic which extends classical logic with default rules. Unfortunately, default logic is undividable. One reason for that is the use of classical logic as its monotonic basis. In this work, we change the default logic monotonic basis and propose a new default logic based on the description logic ALC. This new default logic is decidable and useful to formalize practical reasoning on hierarchical ontology's with exceptions, specially the ones that deals with legal knowledge and reasoning. On the default counterpart, we add some restrictions to the application of defaults in order to obtain nice properties such as coherence and elimination of anomalous extensions. We present the main algorithms used to build the extensions for this logic with its complexity analysis.

We consider simple i∗-style goal models with influence (contribution) links and AND/OR refinement (decomposition), and formalize them by translation into three standard logics that are actively studied in AI Knowledge Representation and Reasoning (KR&R): propositional logic, FOL and description logics (the first formalization is well known). In each case, this provides a semantics for the notation, on which we can base the definition of forward (“what if?”) and backward (“how is this achievable?”) reasoning, of interest to requirements engineers. We consider the manner in which AI KR&R research provides off-the-shelf algorithms that can be used to solve these tasks. We compare the representations by reporting known worst-case complexity results for the reasoning, as well as other criteria such as size/understandability of axiomatization, and ease of extension of modeling language.

This paper aims to propose a model of reasoning based on semantic relations among words and to incorporate it in the inference mechanism of a logic programming language. This model is integrated in a fuzzy logic programming framework and it is implemented into the Bousi~Prolog system by using WordNet. All this process is transparent to the programmer and the reasoning with words is automatic. The lexical semantics between symbols (words) provides us with the ability of reasoning with words and turns the knowledge representation in a more natural and less complex process.

The first aim of this article is to provide readers informally with the basic notions of defeasible and non-monotonic reasoning, logics borrowed from artificial intelligence. It then describes argumentation theory, a paradigm for implementing defeasible reasoning in practice as well as the common multi-layer schema upon which argument-based models are usually built. The second aim is to describe the selection of argument-based applications in the medical and health-care sectors. Finally, the paper will conclude with a summary of the features, which make defeasible reasoning and argumentation theory attractive, that emerge from the applications under review. The target reader is a medical or health-care practitioner, with limited skills in formal knowledge representation and logic, interested in enhancing evidence modelling and aggregation.

The issue of inconsistency handling has attracted a lot of attentions recently. As we know that ontology reasoner will stop work once contradiction happens. In logic reasoning, belief non-revision focuses on the management of contradiction beliefs. In this paper, we try to translate the ontology to formulas in First-order Logic (FOL) with the help of description logic, so belief non-revision reasoning can continue. This approach is a new method to conduct reasoning with inconsistency based on the belief non-revision.

This paper is about extending nonmonotonic description logic with temporal aspects; this attempt permits actually to represent both default and temporal features in concepts definition. The introduction of defaults in the definition of concepts in previous researches has allowed to go beyond the strict limitations on their description and permitted consequently to fully define them; by providing both necessary and sufficient conditions for their representation. This allowed improving the classification process. Contrary to the use of strict knowledge that provides only necessary conditions leaving the concepts partially defined. The nonmonotonic language that allows using defaults in the definition of concepts is AL augmented with default and exception connectors that allow respectively representing default and exception properties in concepts definition. However we frequently need to add the temporal aspect to the nonmonotonic feature as it is the case in causal reasoning, planning process, and action theory. In our case, we will use it in the field of access control. Our aim in this paper is to extend this logic further with temporal connectives to grant the possibility to represent temporal properties of concepts and that by referring to temporal description logic.

Representing and reasoning spatial and temporal information is a key research issue in Computer Science and Artificial Intelligence. In this paper, we introduce tools that produce three novel encodings which translate problems in qualitative spatial and temporal reasoning into logic programs for answer set programming solvers. Each encoding reflects a different type of modeling abstraction. We evaluate our approach with two of the most well known qualitative spatial and temporal reasoning formalisms, the Interval Algebra and Region Connection Calculus. Our results show some surprising findings, including the strong performance of the solver for disjunctive logic programs over the non-disjunctive ones on our benchmark problems.

Epistemic Programming was proposed as a novel program paradigm to program epistemic processes in scientific discovery, and an epistemic programming language and its implementation were also proposed and developed. The paradigm of Epistemic Programming regards conditionals as the subject of computing, takes primary epistemic operations as basic operations of computing, and regards epistemic processes as the subject of programming. In order to provide scientists with a high-level and general-purpose mechanism to deal with belief revision more easily in Epistemic Programming, this paper proposes a truth maintenance system for epistemic programming environments. At first, the paper analyzes features of a truth maintenance system for epistemic programming environment. Then, the paper gives a requirement analysis of the truth maintenance system, and shows a design of the truth maintenance system. The proposed truth maintenance system is suitable for epistemic programming environment, and can deal with replacement of logic systems underlying reasoning in epistemic processes.

Context-awareness is an essential requirement of modern access control models. Organization-Based Access Control (OrBAC) model is a powerful context-aware access control model defined by first-order logic. However, due to the monotonicity nature of the first-order logic, OrBAC suffers from the incapability of making decision based on incomplete context information as well as the definition of default and exception policy rules. This paper proposes augmenting non-monotonicity features to OrBAC using MKNF+ logic, which is a combination of Description Logic (DL) and Answer Set Programming (ASP). Along with the use of DL to define ontology for main entities and context information in OrBAC; MKNF+ rules are used to define access control, default, and exception policy rules. The proposed model inherits the advantages of ontological representation of OrBAC entities and context information (such as interoperability among systems) as well as the ASP advantages in non-monotonic reasoning through closed-world principle and negation as failure. The expressive power of the model is also demonstrated through a case study.

Emerging tools that ease sharing information in online social networks (OSNs) can cause various privacy issues for users. Access control is the main security mechanism in OSNs which is used to tackle such issues. In this paper, a prioritized ontology based access control model for protecting users' information in OSNs is proposed. In the proposed model, description logic (DL) is used for modeling social networks and MKNF+ rules are used for specification of users' access control policies. Using MKNF+, we can have nonmonotonic inference (i.e., closed-world reasoning) in the access control procedure. Conflict among access rules defined by a user in an OSN, is another problem, which is resolved in the proposed model by defining priority levels for the rules in a logical manner. Logical foundation of the model gives accuracy, expressiveness, and inference (of implicit access rules from the explicit ones) to the model, and thus decreases the risk of sharing information in OSNs.

This paper is intended to discus the construction of granular logic system under variable fuzzy criterion condition and the formal representation of the reasoning of granular logic system so as to study the application of granular computing from another perspective and to facilitate the application of granular computing in artificial intelligence with uncertainty. The change law of fuzzy criterion variability can be described by the transformation of qualitative and quantitative attributes in attribute theory. On the basis that the concept and logic computing formula of attribute granule are given, a granular logic system is initially established and the logic reasoning is described using the formalization method of Petri Net, showing that the reasoning is effective and operations of thinking like cognition, recognition, judging and reasoning can be well expressed.

In representing ontologies there are two kinds of default statements: one is the default assertions which say that some concept has some property defaultly, another is the default inheritance of default assertions. The latter actually is a default rule of default rules (called super default rules), that is, a default rule in which the formulas occur in the default are default rules. In this paper we shall give a formal description of such super default rules, a super default theories and the definitions of the extensions of the super default theories.

Trust is a critical issue for communication systems. A theory of trust for a given system consists of a set of rules that describe the trust of agents in the system. In a certain logical framework, the theory is generally established based on the security mechanisms of the system. Such a theory provides a foundation for reasoning about agent beliefs as well as security properties that the system may satisfy. For modelling authentication protocols, many researchers have proposed different logical systems. Since communication systems operate in dynamic environments, it is important to model the temporal aspects of such systems in a systematic way. In this paper, we propose a temporalised belief logic called TBL for formalising and verifying authentication protocols.

Adaptation decisions made by context-aware applications on behalf of users are based on evaluations of current context and preferences of users. This context information is imperfect by nature and can cause applications to behave in ways that users do not expect. Applications that exhibit unwanted behaviour will negatively impact their usability and violate the trust users have in them. Intelligibility and control in applications can help users to understand why they decided to behave in certain ways, and to forgive the applications by enabling users to override the undesirable adaptation. This paper presents a non-monotonic rule based approach (defeasible logic) for modelling user preferences, which serves as the basis of decision-making of application adaptations. It facilitates automatic generation of explanations regarding reasoning of defeasible preferences. The model also supports creation of feedback mechanisms for nontechnical users to formulate their own preferences independently, and modify the adaptation decision process to control applicationbehaviours. Moreover, to demonstrate its applicabilitywe have designed a set of evolvable situations and a context model, which complement the defeasible preferences for building smart home applications to enhance health care of the elderly.

In this paper we review some results about a parallel p-stable solver. We test an algorithm that searches for the p-stable models of a normal program based on stratification of logic programs.

P2P reasoning system can answer queries from not only each peer's local theory but also some other peers related with sharing part of its vocabulary. This paper concentrates on P2P reasoning technology based on description logic, trying to provide an intelligent solution for searching network resources. A P2P reasoning algorithm suitable for description logic called DL-DeCA is proposed in this paper, as well as its communication protocol and system implementation of DL-P2PRS (description logic P2P reasoning system). Experiment results show that DL-P2PRS can work correctly and effectively.

Simulations of naval action estimate the operational performance of warships or submarines for a given scenario. In common models, the operator's reactions are predefined. This is not realistic: the operator's decision can produce unexpected reactions. This paper presents a method to model operator decision in simulations. This method allows to reason about incomplete, revisable and uncertain information: an operator has partial information about his environment only and must revise his decisions. Our method uses a nonmonotonic logic: the rules of behavior are formalized with default logic, to which we added a consideration of time. Our method uses preferences to manage choice between different rules, with simple probabilistic techniques. This method has been implemented in Prolog, interfaced to DCNS simulator framework and applied to a scenario involving two adverse submarines.

This paper presents a logic that extends basicITL with explicit, interleaved programs. The calculus is based on symbolic execution, as previously described. We extend this former work here, by integrating the logic with higher-order logic, adding recursive procedures and rules to reason about fairness. Further, we show how rules for rely-guarantee reasoning can be derived and outline the application of some features to verify concurrent programs in practice. The logic is implemented in the interactive verification environment KIV.

The Web-based intelligent decision support system(Web-IDSS) is pivotal for a Virtual Team (VT) to successfully execute business-related tasks. The current generation of Web-IDSS built on top of semantic web technologies for VTs lacks the capability to provide decision support when underlying information is incomplete and/or contradictory. In this article, we address this limitation of current Web-IDSS through defeasible logic based argumentation formalism. The proposed Web-IDSS uses a hybrid reasoning approach: forward chaining(data-driven) for the construction of arguments over incomplete information, and backward chaining (goal-driven) for conflict identification and resolution with explanation. The proposed Web-IDSS adheres to web standards and publishes the outcome of argumentative reasoning in Argument Interchange Format (AIF).

Based on the theory of graphic method, through graphical the proposition formulas and the reasoning rules, this paper constructs the graphical reasoning process for proposition logic. And in the construction of the reasoning process, rules are made a brief description for using and analysis. Then through an example, explains the graphical construction method and process for proposition formulas and reasoning rules.

Theory of argumentation constitutes a general framework for reasoning and has been widely applied in computational intelligence problems such as nonmonotonic reasoning, decision making and logic programming. To capture the internal structure of arguments, each argument is assigned with a weight, which represents the argument's credibility, to construct a numeric argumentation framework. Within this framework, attacks and preferences are endowed with strengths which are determined by the weights of arguments related. Inspired by the majority rule, properties of attacks or preferences sharing the same target are also explored. The notion of acceptability and extended theory retain the basic machinery of Dung's theory. Finally, an n-person game is formalized in numeric argumentation framework to illustrate the rationality of the new theory.

On the study of knowledge representation and reasoning, there is certain of limitations that one of Description Logic and Horn Rule is used merely. It can solve the Uncertain Reasoning problem that Description Logic is used as the constraint condition of Horn rule reasoning process, which can realize a combined reasoning of Description Logic and Horn Rule. Probability uncertain reasoning algorithm is introduced to Horn rule and the fact part of reasoning system, by using probabilistic logic, by using the resolution result produced by Resolution Principle of Constrain Logic, which can be transformed into solving the corresponding linear optimization equation. The Uncertain Reasoning has the properties of rationality, completeness and decidability.

Recognizing that a community's capability to respond to and recover from disaster depends partly on the strength and effectiveness of its social networks, social network analysis (SNA) has risen to a field having important implications. In particular, many disaster and emergency recovery operations now consider the information provided by social networks to be one of their prime sources of data. The task of integrated social information engineering is to fuse that data to yield meaningful knowledge devoid of contradiction and provide a pathway for discovery of related information. Computing with Words represents an attempt to fuse linguistic information using possibilistic analysis. The approach entails machine learning that fuses contexts consisting of essentially symbolic information for the prediction of an appropriate action(s). SNA is facilitated because the method allows large contexts and crowd sourced approaches, consisting of distinct textual phrases, to be mapped to similar prior experiential knowledge. If this knowledge proves to be erroneous for any reason, then it is a simple matter to supply the correct knowledge for non monotonic learning to occur. The method also supplies an associated possibility, allows for proper responses to be forthcoming, and can be trained / run in parallel. In summary, the paper proper considers the burgeoning field of social information engineering and the automation of its integration by way of transformative reuse.

Inference as a fundamental mechanism of thought is one of the gifted abilities of human beings. Inference can be described as a cognitive process that creates rational causations between a pair of cause and effect based on empirical arguments, formal reasoning, and/or statistical regulations [2, 15, 24, 26]. Conventional logic inferences may be classified as logical arguments, deductive, inductive, abductive, and analogical inferences [4, 9, 10, 11, 15].

This paper investigates an extension to description logic based knowledge reasoning by means of decomposing and rewriting complicated hybrid concepts into partitions. An approach which automatically decomposes the whole knowledge base into description logic compatible and constraints solver, is presented. System architecture for knowledge reasoning is two-fold. First, complex description logics with powerful representation ability and Second, how to reason with the combination of inferences from distributed heterogeneous reasoner.

In this paper we present an account of the problems faced by a mobile robot given an incomplete tour of an unknown environment, and introduce a collection of techniques which can generate successful behaviour even in the presence of such problems. Underlying our approach is the principle that an autonomous system must be motivated to act to gather new knowledge, and to validate and correct existing knowledge. This principle is embodied in Dora, a mobile robot which features the aforementioned techniques: shared representations, non-monotonic reasoning, and goal generation and management. To demonstrate how well this collection of techniques work in real-world situations we present a comprehensive analysis of the Dora system's performance over multiple tours in an indoor environment. In this analysis Dora successfully completed 18 of 21 attempted runs, with all but 3 of these successes requiring one or more of the integrated techniques to recover from problems.

Description Logic is the logical basis of Semantic Web knowledge representation and reasoning, it has strong capacity of expression, but it is not applicable to uncertain knowledge representation and reasoning. Horn rule is a proper subset of first-order predicate logic, which can be determined, its implication expression makes the description closer to a natural description of the problem, but it can not fully express the abundant and hierarchical knowledge in application fields. Therefore the combination of Description Logic and Horn rule, and utilization of Resolution Principle for Constrained Logics, will effectively solve the problem of Uncertain Reasoning. For the subsistent uncertainty of fact and rules in the reasoning system, credibility can be expressed by the interval between 0 and 1, and Confidence Interval calculation for the querying results can be summarized as a process of solving homologous linear optimization equation.

This paper presents a method to model biological systems decision. This method allows to reason about incomplete, revisable and uncertain information: an operator has partial information about his environment only and must revise his decisions. Our method uses a nonmonotonic logic: the rules of behavior are formalized with default logic, to which we added a consideration of time. Our method uses preferences to manage choice between different rules, with simple probabilistic techniques.

Radio Frequency Identification (RFID) technology uses wireless radio technology to automatically track items spanning various locations with little effort or cost. It is used for various applications such as supply chains and health care. Unfortunately, due to the nature of the passive RFID architecture, cloned tags lower the overall accuracy of the system and may allow unauthorised personnel access to sensitive areas/information. To combat this considerable threat, we have proposed a cleaning algorithm designed specifically to identify and flag potential intrusions utilising intelligent analysis coupled with Non-Monotonic Reasoning. We conclude this research by analysing the potential benefits and drawbacks within our proposed methodology.

Some problematic cases, such as collective defeat and odd-length defeat cycles, which tend to be handled incorrectly by all of the current theories of no monotonic reasoning, including default logic and circumscription, have been well recognized in the literature. Although a powerful argument-based approach in the automated defeasible reasoner OSCAR has been proposed and they claim that this theory is able to reason correctly for the problems above all, but we don't consider it to be true completely through careful investigation. It seems to be the consequences of disconnection between epistemic reasoning and practical reasoning and not considering the possible consequences of the decision and individual preferences sufficiently. Following these observations, we propose a scalable probabilistic approach based on Bayesian decision theory that can solve all of the above paradoxes properly and has successfully been used in web of trust and knowledge integration for semantic Grid.

Symmetry had been well studied in classical logics and constraint programming since a decade. Early, Krishna-murthy showed that some tricky formulas admit short proofs when augmenting the propositional logic resolution proof system by the symmetry rule. However, in Artificial Intelligence, we usually manipulate incomplete information and need to include uncertainty to reason on knowledge with exceptions and non-monotonicity. Several non classic logics are introduced for that purpose, but as far as we know, symmetry for these frameworks had not been studied yet. Here, we are interested to extend the notion of symmetry to that non classical logics such as preferential logics, X-logics and default logics, then give a new symmetry inference rule for the X-logics and the default logics. Finally, we show how symmetry reasoning is profitable for these logics and how they handle some symmetries that do not exist in classical logics.

In forensic analysis of visual surveillance data, ‘default reasoning’ can play an important role for deriving plausible semantic conclusions under incomplete and contradictory information about scenes. In this paper, we present an inference framework for default reasoning using Subjective Logic theory. Subjective Logic is a relatively new branch of probabilistic logic that allows explicit representation of ignorance about knowledge in a model called subjective opinion and that also comes with a rich set of operators thereby, having big potential as a tool for belief representation and reasoning. However, its application to visual surveillance is in its infancy and its use for default reasoning is not reported yet. Therefore, the aim of this paper is to bestow the ability of default reasoning on subjective logic and show the feasibility of using the introduced inference framework for visual surveillance. Among the approaches to enable default reasoning, the Bilattice framework is one that is well known and demonstrated for visual surveillance. For deriving the usage of subjective logic for default reasoning, we first discuss the similarity between the partial ignorance concept in subjective logic and the concept of degree of information in Bilattice based structure for multivalued default logic. Then we introduce the inference mechanism for default reasoning by mapping multi-logic-values into subjective opinion and combining operators in subjective logic. Finally, we present some illustrative reasoning examples in typical visual surveillance scenarios.

Radio Frequency Identification technology utilises radio communication between tags and readers to automatically recognise large amounts of items reducing cost in both time and effort. Although there is a wide array of applications which RFID may be applied to, there are several issues that hinder the adoption of the technology into various commercial sectors. One of these problems is that the data recorded by readers are becoming increasingly difficult to decipher in its raw form resulting in complex spatio-temporal aspects. To counter the effects of this issue, we have proposed a concept that employs Clausal Defeasible Logic Non-Monotonic Reasoning to correctly identify a high-level event. From our experimental evaluation, we have shown that our approach surpasses the state-of-the-art probabilistic method in both identifying events and limiting false-positive anomalies into the data set.

Ambient Intelligence environments consist of various devices that collect, process, change and share the available context information. The imperfect nature of context, the open and dynamic nature of ambient environments, and the special characteristics of the involved devices have introduced new research challenges on how to represent and reason with contextual information. Previous work presented a solution based on an extension of Multi-Context Systems through the use of defeasible reasoning to reason efficiently with conflicts. This paper reports on initial experiences gained from the deployment of contextual defeasible reasoning in real environments. We report on the architecture of an implementation on small devices, present the definition and implementation of two concrete application scenarios, and discuss the performance and issues of scalability of the approach.

Maximality is a desirable property of paraconsistent logics, motivated by the aspiration to tolerate inconsistencies, but at the same time retain as much as possible from classical logic. In this paper we introduce a new, strong notion of maximal paraconsistency, which is based on possible extensions of the consequence relation of a logic. We investigate this notion in the framework of finite-valued paraconsistent logics, and show that for every $n>2$ there exists an extensive family of $n$-valued logics, each of which is maximally paraconsistent in our sense, is partial to classical logic, and is not equivalent to any $k$-valued logic with $k

One of the biggest shortcomings in the traditional mobile public information system is lack of a reasonable mechanism for data mining, which can not be reasonably classification of the publish information and scientific analysis of the public feedback information. In order to compensate for this shortfall, we describe a public service information and public feedback-based intelligent mobile public information system based on Brooks' subsumption architecture. In this architecture, we implement the subsumption of lower layers by high layers using nonmonotonic reasoning principles. However, unlike systems that followed Brooks' work, it allows the layers to work in synergy to produce the compound behavior. Study shows that, based on the above structure, the system could be reasonably classification and graded based on the information content, and dynamic adjustment of information assessment strategies, and scientific analysis of public feedback to make the public, government, and public utility companies interaction more efficient.

Normal default theories have many properties exclusively such as semi-monotonicity and existence of extensions, etc, and they have precisely denned proof systems and model theories. Hence, a translation of default theories to normal default theories is necessary in order to make use of the proof systems and model theories of normal default theories to study non-normal default theories. A default is translated to a normal default such that its prerequisite and justification are the prerequisites of the normal default; its consequence is the justification of the normal default. To draw the consequence of a default, the prerequisite of the default is provable and the negation of the justification of the default is not provable, however, to draw the consequence of its normal default, the prerequisite and justification of the default are provable and the negation of the consequence of the default is not provable. Thus a consequence which is derivable from a default may not derivable from its normal default. Hence, in the translation of a default theory to a normal default theory, there is information loss. Therefore, closed world assumption and a priority order on defaults are introduced to study the problem of information loss in the translation. It is proved that under closed world assumption and the priority order on defaults, the translation of default theories with atomic negative defaults to its normal default theories is complete, if they have extensions.

This paper investigates an extension to description logic based knowledge reasoning by means of decomposing and rewriting complicated hybrid concepts into partitions. An approach which automatically decomposes the whole knowledge base into description logic compatible and constraints solver, is presented. System architecture for knowledge reasoning is two-fold. First, complex description logics with powerful representation ability and Second, how to reason with the combination of inferences from distributed heterogeneous reasoner.

The paper presents hardware implementation of the model of commonsense reasoning system based on a new formalism Fuzzy Default Logic (FDL) presented on HSI'09 Conference in Catania. The paper briefly recalls main definitions and algorithms of FDL technique in a form of software oriented procedures. There are presented basic building blocks used for implementation: fuzzy default rules, credible set extraction, hypotheses reduction process and hypotheses assessment stage. Then main elements of the software algorithm of the FDL model are converted to hardware (HDL) models. The entire hardware structure has been implemented in FPGA Xilinx Virtex5 device. The obtained results and examples of experiments are discussed and further research tasks formulated.