Abstract. History based models, introduced by Parikh and Ramanujam, provide a natural mathematical model of social interactive situations. These models offer a ”low level ” description of a social situation — describing the situation in terms of events, sequences of events, and the agents ‘ view of these events. A multi-agent epistemic temporal modal logic can be used to reason about these structures. A number of other models have been proposed in the literature which can be used as a semantics for such a logical language. Most notably, the interpreted systems discussed by Fagin et al. In this paper, we will discuss the differences and similarities between these two mathematical models. In particular, it is shown that these two semantics are modally equivalent. We will conclude with a discussion of a number of questions that are raised when history based models are used to reason about game-theoretic situations. 1

The collaboration between two intelligent agents can be greatly enhanced if a third agent, who has some understanding of the communication between the first two, intervenes giving appropriate information or acting helpfully without having been explicitly involved. The behavior of this third agent, quite common in human interaction, is called overhearing. We present an agent architecture modeling this behavior. In particular, we focus on overhearing based on ontological reasoning; that is, the overhearer semantically selects pieces of communication according to his own knowledge (ontologically organized) and goals. In our

Temporality and uncertainty are important features of real world systems where the state of a system evolves over time and the transition through states depends on uncertain conditions. Examples of such application areas where these concepts matter are smart home systems, disaster management, and robot control etc. Solving problems in such areas usually requires the use of formal mechanisms such as logic systems, statistical methods and other reasoning and decision-making methods. In this chapter, we extend a previously proposed temporal reasoning framework to enable the management of uncertainty based on a many-valued logic. We prove that this new many-valued temporal propositional logic system is sound and complete. We also provide extended reasoning algorithms that can now handle both temporality and uncertainty in an integrated way. We illustrate the framework through a simple but realistic scenario in a smart home application. Decision making is a process of leading to a selection of a course of action among many alternatives and happening all over the world all the time. People try to collect as much information as they can to help them to perform the most appropriate decision for further

ABSTRACT In artificial intelligence, knowledge representation is a combination of data structures and interpretive procedures that leads to knowledgeable behavior. Therefore, it is required to investigate such knowledge representation technique in which knowledge can be easily and efficiently represented in computer. For better result knowledge should be organized in better way. Hence, a structure for that knowledge is required. The knowledge representation techniques are divided in to two categories declarative and procedural .This research paper compares various declarative knowledge representation techniques and proves that predicate logic is a more efficient and more accurate knowledge representation scheme.

Abstract—Recently, formal methods like model checking or theorem proving have been considered efficient tools for software verification. However, when practically applied, those techniques suffer high complexity cost. Combining static analysis with dynamic checking to deal with this problem has been becoming an emerging trend, which results in the introduction of concolic testing technique and its variations. However, the analysis-based verification techniques always assume the availability of full source code of the verified program, which does not always hold in real life contexts. In this paper, we propose an approach to tackle this problem, where our contributed ideas are (i) combining function specification with control flow analysis to deal with sourcemissing function; (ii) generating self-complete programs from incomplete programs by means of concrete execution, thus making them fully verifiable by model checking; and (iii) developing a constraint-based test-case generation technique to significantly reduce the complexity. Our solution has been proved viable when successfully deployed for checking programming work of students. Index Terms—specification-based model checking, concolic testing, constraint-based test-case generation, incomplete programs I.

We give labeled natural deduction systems for a family of tense logics extending the basic linear tense logic Kl. We prove that our systems are sound and complete with respect to the usual Kripke semantics, and that they possess a number of useful normalization properties (in particular, derivations reduce to a normal form that enjoys a subformula property). We also discuss how to extend our systems to capture richer logics like (fragments of) LTL. 1

In Artificial Intelligence there is a need for reasoning about continuous processes, where assertions refer to time intervals rather than time points. Taking our lead from van Benthem’s treatment of interval temporal structures and Halpern and Shoham’s work on intervals, we present a interval temporal logic with two binary relations, precedence and inclusion. We study the logic in its full generality without making any assumptions about the underlying nature of time, be it discrete or dense, linear or branching. We identify two general classes of interval temporal structures, minimal interval structures and van Benthem minimal interval structures. We show that in our interval temporal language, the two classes in fact have the same logic. We go on to prove that the logic of minimal interval structures is complete and decidable, possessing the finite model property, and that the satisfiability problem is PSPACE-complete. In order to establish the complexity result we extend the tableau method introduced by Horrocks et al., which treats transitive and inverse relations only, to also incorporate interaction between relations. We go on to identify some important limitations in the expressive power of our logic, before concluding the paper by raising a number of interesting questions that follow from our work.

Trust is the cornerstone of traditional business transactions and a lack of trust is one of the main inhibitors on the future growth of Electronic Commerce. However, trust is a nebulous social construct that is further complicated by its context dependence. Our assertion is that through study of the contextual artifacts and the process of trust formation in traditional business settings, we can devise a logic-based model that adequately captures the constructs needed to enable the automated creation of trusted Electronic Commerce transactions. In this paper, we introduce a model, called E2T2, which enables the creation of trust-based relationships between online merchants and their consumers.

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