Logic Programs with Multiple Chances

Abstract. In human-like reasoning it often happens that different conditions, partially alternative and hierarchically structured, are mentally grouped in order to derive some conclusion. The hierarchical nature of such knowledge concerns with the possible failure of a chance of deriving a conclusion and the necessity, instead of blocking the reasoning process, of activating a subordinate chance. Traditional logic programming (we refer here to Answer Set Programming) does not allow us to express such situations in a synthetic fashion, since different chances of deriving a conclusion must be distributed over different rules, and conditions enabling the switching among chances must be explicitly represented. We present a new language, relying on Answer Set Programming, which incorporates a new modality able to naturally express the above features. The merits of the proposal about the capability of representing knowledge are shown both by examples and by comparisons with other existing fo...

• Type: Conference
• Year: 2006
• Where: ECAI
• Authors: Francesco Buccafurri, Gianluca Caminiti, Domenico Rosaci
• Keywords: Answer Set Programming, Artificial Intelligence, ECAI 2006, Human-like Reasoning, Traditional Logic Programming