Readings in nonmonotonic reasoning
Readings in nonmonotonic reasoning
Constructive belief and rational representation
Computational Intelligence
General theory of cumulative inference
Proceedings of the 2nd international workshop on Non-monotonic reasoning
Nonmonotonic reasoning, preferential models and cumulative logics
Artificial Intelligence
Probabilistic semantics for nonmonotonic reasoning: a survey
Proceedings of the first international conference on Principles of knowledge representation and reasoning
Artificial Intelligence - Special issue on knowledge representation
Decision analysis and expert systems
AI Magazine
What does a conditional knowledge base entail?
Artificial Intelligence
General patterns in nonmonotonic reasoning
Handbook of logic in artificial intelligence and logic programming (vol. 3)
Qualitative probabilities for default reasoning, belief revision, and causal modeling
Artificial Intelligence
A Maximum Entropy Approach to Nonmonotonic Reasoning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Logic-Based Subsumption Architecture
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
System Z: A Natural Ordering of Defaults with Tractable Applications to Nonmonotonic Reasoning
Proceedings of the 3rd Conference on Theoretical Aspects of Reasoning about Knowledge
Hidden uncertainty in the logical representation of desires
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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In recent years, considerable effort has gone into understanding default reasoning. Most of this effort concentrated on the question of entailment, i.e., what conclusions are warranted by a knowledge-base of defaults. Surprisingly, few works formally examine the general role of defaults. We argue that an examination of this role is necessary in order to understand defaults, and suggest a concrete role for defaults: Defaults simplify our decision-making process, allowing us to make fast, approximately optimal decisions by ignoring certain possible states. In order to formalize this approach, we examine decision making in the framework of decision theory. We use probability and utility to measure the impact of possible states on the decision-making process. More precisely, we examine when a consequence relation, which is the set of default inferences made by an inference system, can be compatible with such a decision-theoretic setup. We characterize general properties that such consequence relations must satisfy and contrast them with previous analysis of default consequence relations in the literature. In particular, we show that such consequence relations must satisfy the properties of cumulative reasoning. Finally, we compare our approach with Poole's decision-theoretic defaults, and show how both can be combined to form an attractive framework for reasoning about decisions.