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Titlebook: Computational Logic in Multi-Agent Systems; 7th International Wo Katsumi Inoue,Ken Satoh,Francesca Toni Conference proceedings 2007 Springe

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Norman M. Spreng,Stefan Dietrichan element in the vector space spanned by the basis of possible worlds, so that belief change corresponds to a linear transformation on this vector space. The compound belief states of multi-agents are treated by using the product tensor of the vector for each agent. In this formulation, the reasoni
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https://doi.org/10.1007/3-540-30980-2ally be useful when, as in other safety-critical scenarios, it is possible to prove that particular properties do or do not hold for all possible encounters. In contrast to symbolic model-checking, answer set programming permits the statement of problems and queries in domain-specific terms as execu
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Norman M. Spreng,Stefan Dietrichy. Similar to .-calculus, . is a succinct and expressive language. It is showed that temporal modalities such as “always”, “sometime” and “until”, and knowledge modalities such as “everyone knows” and “common knowledge” can be expressed in such a logic. Furthermore, we study the model checking techn
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https://doi.org/10.1007/978-3-531-92889-0 CTL* (Extended Computation Tree Logic) and on dynamic logic. Called Commitment and Argument Network (CAN), our formal framework based on this hybrid approach uses three basic elements: social commitments, actions that agents apply to these commitments and arguments that agents use to support their
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https://doi.org/10.1007/978-3-531-92889-0ccount for modeling the intricacies of agent reasoning in context. Using an example of reasoning about perspectives we shown that this model fulfills the formal requirements for a theory of context, and offers a nice explanatory account of contextual reasoning in terms of information flow.
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https://doi.org/10.1007/978-3-663-04933-3y, where each possible world includes CTL. We represent the channel by a reserved set of propositional variables. With this, we revise the definition of . of FIPA; if the channel exists the receiver agent surely learns the information whereas if not the action fails. According to this distinction, t
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