Approximate and commonsense reasoning: From theory to practice
Résumé
This paper provides an overview of present trends in approximate and commonsense reasoning. The different types of reasoning, which can be covered by this generic expression, take place when the available information is either incomplete, or inconsistent, or pervaded with uncertainty, or imprecise and qualitative. The conclusions which are then obtained are usually plausible but uncertain. Yet, approximate or commonsense reasoning is useful in practical problems such as prospect evaluation, diagnosis, forecasting and decision tasks, where better information cannot be got. Classical logic is insufficient for handling these types of reasoning. Different ideas of orderings play a role in these reasoning processes: plausibility orderings between interpretations or situations which are unequally uncertain, similarity orderings with respect to prototypical situations or cases, preference orderings between acts or situations when the problem is a matter of choice. These orderings can be encoded using purely ordinal scales, or scales with a richer structure (when it is meaningful and compatible with the quality of the available information). This general idea of ordering provides a kind of unification between the different reasoning modes and somewhat typifies approximate and commonsense reasoning. Advances in default reasoning, inconsistency handling, data fusion, updating, abductive reasoning, interpolative reasoning, and decision issues in relation with Artificial Intelligence research, are briefly reviewed. Open questions and directions for future research which seem especially important for the development of practical applications are pointed out. The paper is largely based on authors' research experience, and as such, presents a rather personal view, which may not be exempt from some biases.
Domaines
Intelligence artificielle [cs.AI]
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