Recent work shows issues of consistency with explanations, with methods generating local explanations that seem reasonable instance-wise, but that are inconsistent across instances. This suggests not only that instance-wise explanations can be unreliable, but mainly that, when interacting with a system via multiple inputs, a user may actually lose confidence in the system. To better analyse this issue, in this work we treat explanations as objects that can be subject to reasoning and present a formal model of the interactive scenario between user and system, via sequences of inputs, outputs, and explanations. We argue that explanations can be thought of as committing to some model behaviour (even if only prima facie), suggesting a form of entailment, which, we argue, should be thought of as non-monotonic. This allows: 1) to solve some considered inconsistencies in explanation, such as via a specificity relation; 2) to consider properties from the non-monotonic reasoning literature and discuss their desirability, gaining more insight on the interactive explanation scenario.

Default and auto-epistemic reasoning were also well represented, with a number of papers discussing aspects, applications, and implementations of default reasoning systerns. Several papers emphasized nonmonotonic facets of computational vision, natural language understanding, and conimo1i-sense reasoning. Thursday evening, a panel discussion was held, with John McCarthy, Dana Scott, and Richmond Thomason as panelists. Compare it with a merely COMMON LISP (Golden Common Lisp@ Version 1.OO): Golden Common Lisp is a registered trademark of Gold Hill Computers. Our low-key, dignified approach to matchingquality candidates with quality companies will offer you the opportunity to examine your alternatives in a confidential, systematic fashion Openingsarenationwide.

Circumscription formalizes such conjectural reasoning.

Arguments are presented in favor of the answer "yes". The intuitive appeal (or lack thereof) of probabilities is considered briefly. The theoretical adequacies of probabilistic methods are investigated by considering them in light of McCarthy's "typology of uses of non-monotonic reasoning." A quantitative approach which overcomes the usual need for a priori probabilities is presented. Some of the practical advantages of using probabilities in a production system are described.

Considerable attention has been given to the problem of non-monotonic reasoning in a belief function framework. Earlier work (M. Ginsberg) proposed solutions introducing meta-rules which recognized conditional independencies in a probabilistic sense. More recently an e-calculus formulation of default reasoning (J. Pearl) shows that the application of Dempster's rule to a non-monotonic situation produces erroneous results. This paper presents a new belief function interpretation of the problem which combines the rules in a way which is more compatible with probabilistic results and respects conditions of independence necessary for the application of Dempster's combination rule. A new general framework for combining conflicting evidence is also proposed in which the normalization factor becomes modified. This produces more intuitively acceptable results.

Matchmaking arises when supply and demand meet in an electronic marketplace, or when agents search for a web service to perform some task, or even when recruiting agencies match curricula and job profiles. In such open environments, the objective of a matchmaking process is to discover best available offers to a given request. We address the problem of matchmaking from a knowledge representation perspective, with a formalization based on Description Logics. We devise Concept Abduction and Concept Contraction as non-monotonic inferences in Description Logics suitable for modeling matchmaking in a logical framework, and prove some related complexity results. We also present reasonable algorithms for semantic matchmaking based on the devised inferences, and prove that they obey to some commonsense properties. Finally, we report on the implementation of the proposed matchmaking framework, which has been used both as a mediator in e-marketplaces and for semantic web services discovery.

Matchmaking arises when supply and demand meet in an electronic marketplace, or when agents search for a web service to perform some task, or even when recruiting agencies match curricula and job profiles. In such open environments, the objective of a matchmaking process is to discover best available offers to a given request. We address the problem of matchmaking from a knowledge representation perspective, with a formalization based on Description Logics. We devise Concept Abduction and Concept Contraction as non-monotonic inferences in Description Logics suitable for modeling matchmaking in a logical framework, and prove some related complexity results. We also present reasonable algorithms for semantic matchmaking based on the devised inferences, and prove that they obey to some commonsense properties. Finally, we report on the implementation of the proposed matchmaking framework, which has been used both as a mediator in e-marketplaces and for semantic web services discovery.

This paper reviews the history of process-dependent reasoning in AI systems, and argues that it represents an essentially different approach to non-monotonic reasoning from other formalizations. Much of the paper is a basic level tutorial, explaining the issues and providing a framework for understanding the essential features of non-monotonic reasoning.