No abstract

Abductive reasoning algorithms formulate possible hypotheses to explain observed facts using a theory as the basis. These algorithms have been applied to various domains such as diagnosis, planning and interpretation. In general, algorithms for abductive reasoning based on logic present the following disadvantages: (1) they do not allow the explicit declaration of conditions that may affect the reasoning, such as intention, context and belief;(2) they allow little or no consideration for criteria required to select good hypotheses. Using Propositional Logic as its foundation, this study proposes the algorithm Peirce, which operates with a framework that allows one to explicitly include conditions to conduct abductive reasoning and uses a criterion to select good hypotheses that employs metrics to define the explanatory power and complexity of the hypotheses. Experimental results suggest that abductive reasoning performed by humans has the tendency to coincide with the solutions computed by the algorithm Peirce.