PStable semantics for possibilistic logic programs Mauricio Osorio 1 and Juan Carlos Nieves 2 1 Universidad de las Américas - Puebla CENTIA, Sta. Catarina Mártir, Cholula, Puebla, 72820 México osoriomauri@googlemail.com 2 Universitat Politècnica de Catalunya Software Department (LSI) c/Jordi Girona 1-3, E08034, Barcelona, Spain jcnieves@lsi.upc.edu Abstract. Uncertain information is present in many real applications e.g., medi- cal domain, weather forecast, etc. The most common approaches for leading with this information are based on probability however some times; it is difficult to find suitable probabilities about some events. In this paper, we present a pos- sibilistic logic programming approach which is based on possibilistic logic and PStable semantics. Possibilistic logic is a logic of uncertainty tailored for reason- ing under incomplete evidence and Pstable Semantics is a solid semantics which emerges from the fusion of non-monotonic reasoning and logic programming; moreover it is able to express answer set semantics, and has strong connections with paraconsistent logics. 1 Introduction To find a representation of the information under uncertainty has been subject of much debate. For those steeped in probability, there is only one appropriate model for numeric uncertainty, and that is probability. But probability has its problems. For one thing, the numbers are not always available. For another, the commitment to numbers means that any two events must be comparable in terms of probability: either one event is more probable than the other, or they have equal probability [4]. In fact, in [7], McCarthy and Hayes pointed out that attaching probabilities to a statement has some objections. For instance: The information necessary to assign numerical probabilities is not ordinary available. Therefore, a formalism that required numerical probabilities would be epistemologically inadequate [7]. Hence it is not surprising that many other representations of uncertainty have been con- sidered in the literature. For instance in the MYCIN project which is one of the clearest representatives of the experimental side of Artificial Intelligence (IA) was shown that probability theory has limitations for developing automated assistance for medical diag- nosis [2]. In this project, it was adopted a less formal model. This model uses estimates provided by expert physicians that reflect the tendency of a piece of evidence to prove or disprove a hypothesis. The syntax adopted by MYCIN was based on IF-THEN rules with certainty factors. The following is an English version of one of MYCIN’s rules: