F. Giunchiglia, L. Serafini, E. Giunchiglia, and M. Frixione. Non-omniscient belief as context-based reasoning. In Proc. of 13th IJCAI Conf., Chambery, France, pages 548--554, 1993.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....(underlined by the fact that actors in order to change their attitude even send messages to themselves) The fact that the inhabited environment is left implicit additionally contrasts with our framework. A logical treatment of modular agent systems; called a Logic of Contexts is described in [7]. Giunchiglia et al. have developed a formalism in which agent systems are hierarchies of logical theories, called contexts, connected by lifting and lowering bridge rules. A lifting rule ensures that if some specified formula j holds in a sub context, the associated formula j holds in the ....

F. Giunchiglia, L. Serafini, E. Giunchiglia, and M. Frixione. Non-omniscient belief as context-based reasoning. In IJCAI-93, pages 548--554, 1993.

....(or: how we can do without modal logics) 10] III Applications 1. Bouquet P. Giunchiglia F. Reasoning about Theory Adequacy. A new Solution to the Quali cation Problem [4] 2. Giunchiglia F. Sera ni L. Giunchiglia E. Frixione M. Non Omniscient Belief as Context Based Reasoning [11]. 3. Cimatti A. Sera ni L. Multi Agent Reasoning with Belief Contexts: the Approach and a Case Study [6] 4. Benerecetti M. Bouquet P. Ghidini C. Formalizing belief report. The approach and a case study [1] 5. Ghidini C. Sera ni L. Using wrapper agents to answer queries in ....

F. Giunchiglia, L. Serani, E. Giunchiglia, and M. Frixione. NonOmniscient Belief as Context-Based Reasoning. In Proc. of the 13th International Joint Conference on Articial Intelligence, pages 548-554, Chambery, France, 1993. Also IRST-Technical Report 9206-03, IRST, Trento, Italy.

....[3] by the di erent (initial) axioms satis ed in each observer s context and the relations between them are explicitly stated as axioms in context . Belief contexts. LMS and MCS have been applied to formalize di erent aspects of intentional contexts, and in particular belief contexts (see e.g. [15, 6]) An example is a puzzle described in [2] where LMS and MCS are used to solve the problem of the opaque and transparent reading of belief reports. A computer program knows that Mr. A believes that the president of the local football team is Mr. M and that Mr. B believes that the president ....

F. Giunchiglia, L. Serani, E. Giunchiglia, and M. Frixione. Non-Omniscient Belief as Context-Based Reasoning. In Proc. of the 13th International Joint Conference on Articial Intelligence, pages 548-554, Chambery, France, 1993.

....possible events and their probability distribution. Note that the distinction between foreground background process or objects implies di erent formalisms that are related to each other according to some (possibly hierarchical) structures. In this sense, the work proposed by Giunchiglia et al. [3, 4] provide the right framework to formalize such concepts. This formalized background object can be called context , and we can then study how the foreground processes interact dynamically with the context: how the context determines the foreground process s outcome and how the foreground ....

E. Giunchiglia, F. Giunchiglia, L. Serani, and M. Frixione. Non-omniscient belief as context-based reasoning. In IJCAI-93, 1993.

....such as belief. Montague s intensional logic remedies this problem by introducing a third primitive type w, the type of possible worlds. This makes available a type Prop = hw; ti of propositions. Treating propositions as sets of possible worlds is, of course, problematic. See, for instance, [12, 7]. But it is an approach that has been pursued with some success in philosophical logic, computer science, economics, and natural language semantics. It is certainly possible to generalize the possible worlds approach to intensionality to obtain a less restrictive account of propositions. But it ....

Fausto Giunchiglia, Luciano Serani, Enrico Giunchiglia, and Marcello Frixione. Non-omniscient belief as context-based reasoning. In Ruzena Bajcsy, editor, Proceedings of the Thirteenth International Joint Conference on Articial Intelligence, pages 548-554, San Mateo, California, 1993. Morgan Kaufmann.

....beliefs has raised very difficult problems. Modal logics [2] are the well known formalism for representing beliefs and, more generally, propositional attitudes, the one which has been most widely proposed and studied in the logic and philosophical literature (see [13] Following the analisys of [10, 11], the approaches that make use of a single theory suffer from many problems: lack of modularity. In a unique theory it is very hard to represent all the agents knowledge, their usually very different reasoning capabilities, the usually very complicated interactions among them; ....

....reasoning in which knowledge and beliefs of an agent refer to different states (or agent s frames of mind) and different kinds of knowledge (e.g. implicit, explicit, local knowledge) are represented by different modal operators added to the language. Another attempt is due to Giunchiglia et al. [10, 11, 7] and is based on the notion of context. Giunchiglia et al. present formal systems for the representation of propositional attitudes and multi agent systems based on the framework of ML systems 1 . Such systems provide the expressibility of normal modal logics [10] and of the most common non ....

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F. Giunchiglia, L. Serafini, E. Giunchiglia, and M. Frixione. Non-Omniscient Belief as Context-Based Reasoning. In Proc. of the 13th International Joint Conference on Artificial Intelligence, pages 548--554, Chambery, France, 1993.

....do not explicitly make use of context in explanation and in their monolithic rather than incremental knowledge acquisition. Mechanisms for extending them for incremental knowledge acquisition are described in section 5.0. It is also interesting to note that in currently published literature [Giunchiglia 93; McCarthy 93; Lesprance 93; Guha 91] some context formalisms based on first order logic are presented but are too simple to adequately cover the problems of knowledge representation in developing real world complex systems such as JETA or SEPT. These formalisms also concentrate on the movement ....

Giunchiglia F., Non-omniscient belief as context-based reasoning, Proceedings of the 13th IJCAI, 1993, pp. 548-554.

....In this work, several and important concepts (such as the formula Ist(c,p) lifting, entering and exiting contexts) were introduced and formalised. F. Giunchiglia was the first to shift the focus explicitly from context to contextual 1 reasoning in his 1993 paper on Contextual Reasoning (Giunchiglia 1993). His main motivation was the problem of locality, namely the problem of modelling reasoning which uses only a subset of what reasoners actually know about the world. The proposed framework, called MultiContext Systems (MCS) was then applied to formalise intensional contexts, in particular belief ....

....motivation was the problem of locality, namely the problem of modelling reasoning which uses only a subset of what reasoners actually know about the world. The proposed framework, called MultiContext Systems (MCS) was then applied to formalise intensional contexts, in particular belief contexts (Giunchiglia, Serafini, Giunchiglia Frixione 1993, Cimatti Serafini 1995, Benerecetti, Bouquet Ghidini 1998) We refer the reader to (Akman Surav 1996) for a good discussion of the work on the formalisation of context in AI. The interest in context is not limited to AI, though. On the contrary, it is discussed and used in various ....

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Giunchiglia, F., Serafini, L., Giunchiglia, E. & Frixione, M. (1993). Non-Omniscient Belief as Context-Based Reasoning, Proc. of the 13th International Joint Conference on Artificial Intelligence, Chambery, France, pp. 548--554. Also IRST-Technical Report 9206-03, IRST, Trento, Italy.

....that relate one context to another, lifting axioms. If OE being true in a context c implied that was true in a context c 0 , he would write, ist(c; OE) ist(c 0 ; While McCarthy was developing the idea of contexts a student of his, Guha (1991) applied the ideas to Cyc (Lenat 1995) Giunchiglia (1993) introduced the notion of a multi language system. Based on intuitions from proof theory, this attempts captures how patterns of reasoning in different languages can be integrated. Giunchiglia et al. 1993) address issues of non omniscient belief, while our contexts are all logically closed. ....

....the idea of contexts a student of his, Guha (1991) applied the ideas to Cyc (Lenat 1995) Giunchiglia (1993) introduced the notion of a multi language system. Based on intuitions from proof theory, this attempts captures how patterns of reasoning in different languages can be integrated. Giunchiglia et al. 1993) address issues of non omniscient belief, while our contexts are all logically closed. Clearly there is a clear relationship between how patterns of reasoning are related, and theories are related. 4.2 Quoting Approaches Montague (1963) showed that modal logic cannot be mimiced by a quoting ....

Giunchiglia, F., L. Serafini, E. Giunchiglia, and M. Frixione. 1993. Non-omniscient belief as context-based reasoning. In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence.

.... contexts have been introduced to AI, some work has been done both to understand all the possible ways contexts are involved in commonsense reasoning (see e.g. 19] Guha s thesis [15] Shoham s discussion [25] and McCarthy s notes [20] and to reify such a notion into a formal system (see e.g. [17, 6, 13, 12, 3, 5, 7, 10]) In particular, both in [3] and in [10] the isth; i predicate is interpreted as a modal operator and contexts are interpreted in [3] through a multimodal setting and in [10] via fibring. Both the approaches are quite sophisticated: they provide a sound and complete calculus and lift the ....

F. Giunchiglia, L. Serafini, E. Giunchiglia, and M. Frixione. Non-Omniscient Belief as Context-Based Reasoning. In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence (IJCAI-93), pages 548--554, Chambery, France, 1993. Also IRST-Technical Report 9206-03, IRST, Trento, Italy.

No context found.

F. Giunchiglia, L. Serafini, E. Giunchiglia, and M. Frixione. Non-Omniscient Belief as Context-Based Reasoning. In Proc. of the 13th International Joint Conference on Artificial Intelligence, pages 548--554, Chambery, France, 1993. Also IRSTTechnical Report 9206-03, IRST, Trento, Italy.

No context found.

F. Giunchiglia, L. Serafini, E. Giunchiglia, and M. Frixione. Non-Omniscient Belief as Context-Based Reasoning. In Proc. of the 13th International Joint Conference on Artificial Intelligence, pages 548--554, Chambery, France, 1993. Also IRST-Technical Report 9206-03, IRST, Trento, Italy.

No context found.

F. Giunchiglia, L. Serafini, E. Giunchiglia, and M. Frixione. Non-Omniscient Belief as Context-Based Reasoning. In Proc. of the 13th International Joint Conference on Artificial Intelligence, pages 548--554, Chambery, France, 1993. Also IRST-Technical Report 9206-03, IRST, Trento, Italy.

....the representation of propositional attitudes. To keep things simple and more similar to MK we consider the single agent case. The metapredicate (see page 7) in MBK is the unary predicate Bl which intuitively stands for belief. The generalization to the multiagent case is straightforward. [13, 15] reports a detailed description of MBK and other related systems for the representation of propositional attitudes in a multiagent environment. The idea underlying the formalization of propositional attitudes is that there is an agent, let us call him a, usually thought of as the computer itself ....

F. Giunchiglia, L. Serani, E. Giunchiglia, and M. Frixione. Non-Omniscient Belief as Context-Based Reasoning. Technical Report 9206-03, IRST, Trento, Italy, 1992. Submitted to IJCAI-93.

.... then, context has been used in di erent types of applications in KRR: designing and building large common sense knowledge bases (see, for example, the project called CYC [39, 34] the largest knowledge base ever built) the formalization of theoretical issues concerning reasoning about beliefs [33, 28, 6, 21, 23]; the formalization of meta reasoning and propositional attitudes [32] the formalization of reasoning with viewpoints [4] reasoning about action [8] modeling of di erent aspects of agents and multi agent systems [7, 16] modeling dialog, argumentation, and information integration in electronic ....

....of belief context (also called view) 7] A view [belief context] is a representation of a collection of beliefs 8 [16] uses the notion of belief context introduced in this section to solve a well known puzzle involving reasoning about belief and ignorance, namely the Three Wise Men problem. In [33, 28] the representation of an ideal and real reasoner using belief context is thoroughly discussed. In [6] belief contexts are used to solve the problem of the opaque and transparent reading of belief reports. In [21, 23] the representation of resource bounded deliberative agents is discussed. 21 ....

[Article contains additional citation context not shown here]

F. Giunchiglia, L. Serani, E. Giunchiglia, and M. Frixione. NonOmniscient Belief as Context-Based Reasoning. In Proc. of the 13th International Joint Conference on Articial Intelligence, pages 548{ 554, Chambery, France, 1993. Also IRST-Technical Report 9206-03, IRST, Trento, Italy.

No context found.

F. Giunchiglia, L. Serafini, E. Giunchiglia, and M. Frixione. Non-Omniscient Belief as Context-Based Reasoning. In Proc. of the 13th International Joint Conference on Artificial Intelligence, pages 548--554, Chambery, France, 1993. Also IRST-Technical Report 9206-03, IRST, Trento, Italy.

....real reasoner or a real observer we mean that such a reasoner or observer believes too little or too much (i.e. it is incomplete or incorrect) with respect to a reasoner or observer taken as reference. This intuition is already informally articulated, even if limited to beliefs and reasoners, in [16]. In particular, in that paper a reasoner is defined real relatively to another reasoner, independently of (what we have called here) the belief system of which it is part. However, the formalization of these ideas is more complex than it might seem, and, as the technical development discussed ....

....relatively to another reasoner, independently of (what we have called here) the belief system of which it is part. However, the formalization of these ideas is more complex than it might seem, and, as the technical development discussed below shows, the notion of reality informally introduced in [16] is not correct. The key observation is that two reasoners or observers cannot be compared independently of the belief system of which they are part. The proofs of the theorems in this Section are reported in Appendix B. 4.1 Realizing MR Gamma systems The starting point is to define when a ....

[Article contains additional citation context not shown here]

F. Giunchiglia, L. Serafini, E. Giunchiglia, and M. Frixione. Non-Omniscient Belief as Context-Based Reasoning. In Proc. of the 13th International Joint Conference on Artificial Intelligence, pages 548--554, Chambery, France, 1993. Also IRST-Technical Report 9206-03, IRST, Trento, Italy.

....on two basic intuitions. The first is that modal reasoning can be implemented as an appropriate composition of reasoning inside multiple propositional theories (or models, if one thinks of satisfiability) GS94] shows how this can be done for provability in the most common normal modal logics; GSGF93] extends these results to various non normal modal logics. Similar ideas are implicit, even if never spelled out as such, in the tableaux for normal modal logics (see, e.g. Fit88, Mas94] The second is that propositional reasoning can be performed very efficiently This work has benefited ....

F. Giunchiglia, L. Serafini, E. Giunchiglia, and M. Frixione. NonOmniscient Belief as Context-Based Reasoning. In Proc. of the 13th International Joint Conference on Artificial Intelligence, pages 548--554, Chambery, France, 1993. Also IRST-Technical Report 9206-03, IRST, Trento, Italy.

....and reasoning about belief. 1 Introduction Contexts are an important topic in many research areas, for example in the semantics of natural language (see, e.g. Kaplan, 1978] and in computational linguistics (see, e.g. Fauconnier, 1985] Lately, contexts have been independently proposed in [Giunchiglia, 1993] and [McCarthy, 1987] as an important means for formalizing (certain forms of) reasoning. In [Giunchiglia, 1993] contexts are seen as a tool for formalizing the locality of reasoning, while in [McCarthy, 1987] contexts are seen as a way for solving the problem of generality. Coherently with these ....

....in the semantics of natural language (see, e.g. Kaplan, 1978] and in computational linguistics (see, e.g. Fauconnier, 1985] Lately, contexts have been independently proposed in [Giunchiglia, 1993] and [McCarthy, 1987] as an important means for formalizing (certain forms of) reasoning. In [Giunchiglia, 1993] contexts are seen as a tool for formalizing the locality of reasoning, while in [McCarthy, 1987] contexts are seen as a way for solving the problem of generality. Coherently with these two proposals, contexts have been used in various applications, e.g. in the integration of heterogeneous ....

[Article contains additional citation context not shown here]

F. Giunchiglia, L. Serafini, E. Giunchiglia, and M. Frixione. Non-Omniscient Belief as Context-Based Reasoning. In Proc. of the 13th International Joint Conference on Artificial Intelligence, pages 548--554, Chambery, France, 1993. Also IRST-Technical Report 9206-03, IRST, Trento, Italy.

....the different aspects of belief in an agent (e.g.the antecedents of a belief) The solution is to replace a single unanalysed belief modality with a family of modalitites corresponding to different sources of information (p. 59) 5 Attempts have been made by Giunchiglia, Serafini, and Frixione [9], Giunchiglia and Serafini [8] An intermediate approach is the syntactic approach by Konolige [12] with a modal logic where the belief operator is interpreted on a set of sentences. the identity of I and he , and the inferences available to somebody who acknowledges the identity. These two ....

F. Giunchiglia, L. Serafini, E. Giunchiglia, and M. Frixione. Non-Omniscient Belief as Context-Based Reasoning. In Proc. of the 13th International Joint Conference on Artificial Intelligence, pages 548--554, Chambery, France, 1993. Also IRSTTechnical Report 9206-03, IRST, Trento, Italy.

No context found.

F. Giunchiglia, L. Serafini, E. Giunchiglia, and M. Frixione. Non-omniscient belief as context-based reasoning. In Proc. of 13th IJCAI Conf., Chambery, France, pages 548--554, 1993.

No context found.

F. Giunchiglia, L. Serafini, E. Giunchiglia, and M. Frixione. Non-Omniscient Belief as Context-Based Reasoning. In Proc. of the 13th International Joint Conference on Artificial Intelligence, pages 548--554, Chambery, France, 1993. Also IRST-Technical Report 9206-03, IRST, Trento, Italy.

No context found.

F. Giunchiglia, L. Serafini, E. Giunchiglia, and M. Frixione. Non-Omniscient Belief as Context-Based Reasoning. In Proc. of the 13th International Joint Conference on Artificial Intelligence, pages 548--554, Chambery, France, 1993. Also IRST-Technical Report 9206-03, IRST, Trento, Italy.

No context found.

F. Giunchiglia, L. Serafini, E. Giunchiglia, and M. Frixione. Non-Omniscient Belief as Context-Based Reasoning. In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence (IJCAI-93), pages 548--554, Chambery, France, 1993. Also IRST-Technical Report 9206-03, IRST, Trento, Italy.

No context found.

F. Giunchiglia, L. Serafini, E. Giunchiglia, and M. Frixione. Non-Omniscient Belief as Context-Based Reasoning. In Proc. of the 13th International Joint Conference on Artificial Intelligence, pages 548--554, Chambery, France, 1993. Also IRST-Technical Report 9206-03, IRST, Trento, Italy.

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