Abstract
This paper is concerned with providing a context based logic (language + semantics) for the representation of agents’s beliefs. While different approaches that make use of a single theory have been proposed in order to model agent’s beliefs, such as modal logics, these often suffer from problems, as lack of modularity, logical omniscence, and dissimilarity with implementations. A partial solution to these problems is to distribute the agent’s knowledge into different and separated modules which interact each others. Our approach is to provide these modules, but in the form of (multi) contexts, each one with its own local language and semantics, and to model the relations among modules as compatibility relations among contexts. We extend here this approach to capture important aspects of “ideal” agents, namely their logically omniscent nature, and of “real” agents, namely their non logically omniscent nature due to some resource-boundedness. The logic we use is based on a logic for contextual reasoning, called Local Models Semantics, which allows a (multi) context-based representation of agent’s belief. A tableau system for a simple instance of such a logic is also presented.
Visiting Research Fellow from University of Trento, Italy, supported by the Italian National Research Council (CNR)
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
M. E. Bratman, D. J. Israel, and M. E. Pollack. Plans and Resource-Bounded Practical Reasoning. Computational Intelligence, 4(4), 1988.
B. F. Chellas. Modal Logic — an Introduction. Cambridge University Press, 1980.
A. Cimatti and L. Serafini. Mechanizing Multi-Agent Reasoning with Belief Confitexts. In Practical Reasoning, International Conference on Formal and Applied Practical Reasoning, FAPR’96, number 1085 in Lecture Notes in Artificial Intelligence, pages 694–696. Springer, 1996.
R. Fagin and J.Y. Halpern. Belief, awareness, and limited reasoning. Artificial Intelligence, 34:39–76, 1988.
R. Fagin, J.Y. Halpern, Y. Moses, and M. Y. Vardi. Reasoning about knowledge. MIT Press, 1995.
M. Fisher and C. Ghidini. Programming Resource-Bounded Deliberative Agents. In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI’99), 1999. To appear.
E. Giunchiglia and F. Giunchiglia. Ideal and Real Belief about Belief. In Practical Reasoning, International Conference on Formal and Applied Practical Reasoning, FAPR’96, number 1085 in Lecture Notes in Artificial Intelligence, pages 261–275. Springer Verlag, 1996.
F. Giunchiglia and C. Ghidini. A Local Models Semantics for Propositional Attitudes. In Proceedings of the 1st International and Interdisciplinary Conference on Modeling and Using Context (CONTEXT-97), pages 363–372, Rio de Jeneiro, Brazil, 1997.
F. Giunchiglia and C. Ghidini. Local Models Semantics, or Contextual Reasoning = Locality + Compatibility. In Proceedings of the Sixth International Conference on Principles of Knowledge Representation and Reasoning (KR’98), pages 282–289, Trento, 1998. Morgan Kaufmann.
F. Giunchiglia and L. Serafini. Multilanguage hierarchical logics (or: how we can do without modal logics). Artificial Intelligence, 65:29–70, 1994.
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.
A. R. Haas. A Syntactic Theory of Belief and Action. Artificial Intelligence, 28:245–292, 1986.
J. Hintikka. Knowledge and Belief. Cornell University Press, Ithaca, NY, 1962.
J. Hintikka. Impossible possible worlds vindicated. Journal of Philosophical Logic, 4:475–484, 1975.
I.A. Langevelde, van, A.W. Philipsen, and J. Treur. A Compositional Architecture for Simple Design Formally Specified in DESIRE. In J. Treur and T. Wetter, editors, Formal Specification of Complex Reasoning Systems. Ellis Horwood, 1993.
F. Massacci. Strongly analytic tableaux for normal modal logics. In Proc. of the 12th Conference on Automated Deduction, 1994.
J. Treur. On the Use of Reflection Principles in Modelling Complex Reasoning. Internation Journal of Intelligent Systems, 6:277–294, 1991.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ghidini, C. (1999). Modelling (Un)Bounded Beliefs. In: Bouquet, P., Benerecetti, M., Serafini, L., Brézillon, P., Castellani, F. (eds) Modeling and Using Context. CONTEXT 1999. Lecture Notes in Computer Science(), vol 1688. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48315-2_12
Download citation
DOI: https://doi.org/10.1007/3-540-48315-2_12
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66432-1
Online ISBN: 978-3-540-48315-1
eBook Packages: Springer Book Archive