Abstract
In a previous paper [5] we presented a combination of the dynamic logic programming paradigm proposed by J. J. Alferes et al. [1],[10] and a version of KS-agents proposed by Kowalski and Sadri [7]. In the resulting framework, rational, reactive agents can dynamically change their own knowledge bases as well as their own goals. In particular, at every iteration of an observe-think-act cycle, the agent can make observations, learn new facts and new rules from the environment, and then it can update its knowledge accordingly. The agent can also receive a piece of information that contrasts with its knowledge. To solve eventual cases of contradiction within the theory of an agent, techniques of contradiction removal and preferences among several sources can be adopted [8]. The actions of an agent are modeled by means of updates, inspired by the approach in [3]. A semantic characterization of updates is given in [1] as a generalization of the stable model semantics of normal logic programs [6]. Such a semantics is generalized to the three-valued case in [3], which enable us to update programs under the well-founded semantics.
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Dell’Acqua, P., Pereira, L.M. (2001). Enabling Agents to Update Their Knowledge and to Prefer. In: Brazdil, P., Jorge, A. (eds) Progress in Artificial Intelligence. EPIA 2001. Lecture Notes in Computer Science(), vol 2258. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45329-6_20
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DOI: https://doi.org/10.1007/3-540-45329-6_20
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