Maria R. Cravo

SNePSwD: A newcomer to the SNePS family

Abstract SNePS 2.1 is a knowledge representation and reasoning system that records the dependencies among propositions that are needed to perform a revision of beliefs when a contradiction is detected. The reasoning of SNePS 2.1 is based on a monotonic logic and the system has no provisos for performing an automatic revision of beliefs. In this paper we present SNePSwD that extends the capabilities of SNePS 2.1 along two dimensions: (1) it is able to represent default rules and to perform default reasoning, i.e. the logic underlying SNePSwD is non-monotonic; (2) it accepts the specification of preferences between hypotheses and uses them to decide which hypotheses to discard to resolve a contradiction. This latter possibility allows a semi-automatic contradiction resolution (in some cases, even completely automatic). We discuss the motivations for the creation of SNePSwD, present the form of default rules it uses, discuss the meaning of each of the three kinds of consequence, and describe how preferences ...

Permissive Belief Revision

We propose a new operation of belief revision, called permissive belief revision. The underlying idea of permissive belief revision is to replace the beliefs that are abandoned by traditional theories with weaker ones, entailed by them, that still keep the resulting belief set consistent. This framework allows us to keep more beliefs than what is usual using existent belief base-based revision theories.

Affective revision

Moods and emotions influence human reasoning, most of the time in a positive way. One aspect of reasoning is the revision of beliefs, i.e., how to change a set of beliefs in order to incorporate new information that conflicts with the existing beliefs. We incorporate two influences of affective states on belief maintenance identified by psychologists, in a AI belief revision operation. On one hand, we present an alternative operation to conventional Belief Revision, Affective Revision, that determines the preference between new and old information based on the mood of the agent revising its beliefs. On the other, we show how beliefs can be automatically ordered, in terms of resistance to change, based on (among other aspects) the influence of emotion anticipations on the strength of beliefs.

A Unified Approach to Default Reasoning and Belief Revision

We present a unified approach to three important areas of AI: nonmonotonic logics, belief revision theories, and belief revision systems. The nonmonotonic logic we present, SWMC, is appropriate to support belief revision systems, because it keeps a track of dependencies between formulas. Another distinguishing feature of SWMC is the distinction between what follows soundly from a set of premises, and what can plausibly be concluded. In what concerns belief revision theories, we present a theory based on a nonmonotonic logic, SWMC, which, to the best of our knowledge, is a novel approach. Finally, we use an implementation of a belief revision system based on the logic and the belief revision theory, to illustrate these formalisms.

A Practical System for Defeasible Reasoning and Belief Revision

We presented a computational system capable of defeasible reasoning, of revising its beliefs, and which uses truth maintenance techniques to maintain its set of beliefs.

Being Aware of Assumptions

We present a nonmonotonic logic, SWMC (after Shapiro, Wand, Martins and Cravo) intended to support belief revision systems. SWMC allows the use of default rules and exceptions to default rules. SWMC associates each derived wff with the hypotheses, and assumptions underlying its derivation, and can thus be used to support belief revision systems. SWMC distinguishes three kinds of conclusions that can be drawn from a set of hypotheses: sound conclusions, plausible conclusions, and conceivable conclusions.

Extending SNePSwD with Permissive Belief Revision

In this paper we present an extension to SNePSwD, a belief revision system which is capable of default reasoning, and belief revision. This extension pertains to a new way of performing belief revision, called permissive belief revision. Typically, to get rid of a contradiction in a set of beliefs, belief revision theories abandon one or more beliefs from that set. The idea behind permissive revision is to take the abandoned beliefs, weaken them, and add the weakened beliefs to the result of classical (in the sense of non-permissive) revision. So, the advantage of permissive revision over classical revision is that in some situations it allows us to keep more information.In another direction, we used SNePSwD dependency records to avoid repeating inferences of formulas that were initially derivable from the abandoned beliefs, and are still derivable from the weakened beliefs.