New Directions for Intelligent Tutoring Systems pp 198-212 | Cite as
A Structure for Epistemic States
Abstract
In this paper, we investigate structure and the rules that should underlie a computer program that is capable of revising its beliefs or opinions. Such a program maintains a model of its environment, which is updated to reflect perceived changes in the environment. This model is stored in a knowledge base, and the program draws logical inferences from the information in the knowledge base. All the inferences drawn are added to the knowledge base.
Among the propositions in the knowledge base, there are some in which the program believes, and there may be others in which the program does not believe. Inputs from the outside world or reasoning carried out by the program may lead to the detection of contradictions, in which case the program has to revise its beliefs in order to get rid of the contradiction and to accommodate the new information.
Keywords
Belief revision truth maintenance systems reasoningPreview
Unable to display preview. Download preview PDF.
References
- Anderson A., Belnap N.: Entailment: The Logic of Relevance and Necessity Vol.1, Princeton, NJ: Princeton University Press, 1975.MATHGoogle Scholar
- Gärdenfors P.: Knowledge in Flux: Modeling the Dynamics ofEpistemic States, Cambridge, MA: MIT Press, 1988.Google Scholar
- Gärdenfors P., Makinson D.: Revisions of knowledge systems using epistemic entrenchment, Proc. of the Second Conference on Theoretical Aspects of Reasoning about Knowledge, Vardi (ed.), Los Altos, CA: Morgan Kaufmann, pp. 83–95, 1988.Google Scholar
- Harman G.: Change in View: Principles of Reasoning, Cambridge, MA: The MIT Press, 1986.Google Scholar
- Martins J.: The truth, the whole truth, and nothing but the truth: an indexed bibliography to the literature on truth maintenance systems, AI Magazine 11, 1990.Google Scholar
- Martins J.P., Shapiro S.C.: Reasoning in multiple belief spaces, Proc. of the Eight International Joint Conference on Artificial Intelligence, pp. 370–373, Los Altos, CA: William Kaufmann, 1983.Google Scholar
- Martins J., Shapiro S.C.: A model for belief revision, Artificial Intelligence 35, 1, pp. 25–79, 1988.CrossRefMATHMathSciNetGoogle Scholar
- Shapiro S.C.: The SNePS semantic network processing system. In: Associative Networks: Representation and Use of Knowledge by Computers, N. Findler (ed.), New York: Academic Press, pp.179–203, 1979.Google Scholar
- Shapiro S.C., Martins J.P.: Recent advances and developments: the SNePS 2.1 report, Current Trends in SNePS — Semantic Network Processing System: Proc. of the First Annual Workshop, Kumar (ed.), pp. 1–13, Lecture Notes in Artificial Intelligence 437, Springer-Verlag, 1990.CrossRefGoogle Scholar
- Shapiro S.C., Rapaport W.: SNePS considered as a fully intensional propositional semantic network. In: The Knowledge Frontier: Essays in the Representation of Knowledge, McCalla, Cercone (eds.), New York: Springer-Verlag, pp. 262–315, 1987.Google Scholar
- Shapiro S.C., Wand M.: The relevance of relevance, Technical Report No.46, Bloomington, IN: Computer Science Department, Indiana University, 1976.Google Scholar
- Stalnaker R.: Inquiry, Cambridge, MA: MIT Press, 1984.Google Scholar