Modelling Agent Reasoning in a Logic Programming Framework for Possibilistic Argumentation
Carlos Iván Chesñevar, Guillermo Ricardo Simari, Teresa Alsinet · 2004
MAS) involves finding an adequate formalization of an agent's knowledge to perform defeasible inferences in a computationally effective way. In the last years, argument-based approaches have proven to be an attractive setting to achieve this goal. Dealing with uncertainty and fuzziness associated with the available knowledge are also common requirements in MAS. Such features, however, are not embedded in most argument-based formalisms. Possibilistic Defeasible Logic Programming (P-DeLP) has recently appeared as an alternative to solve the above problem. P-DeLP is a logic programming language which combines features from argumentation theory and logic programming, incorporating as well the treatment of possibilistic uncertainty and fuzzy knowledge at object-language level. This paper describes how P-DeLP can be applied in the context of formalizing an agent's beliefs and perceptions, along with an argumentative inference procedure to determine which of the agent's beliefs are ultimately accepted (or warranted).