Operations Research
Reasoning about change: time and causation from the standpoint of artificial intelligence
Reasoning about change: time and causation from the standpoint of artificial intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A model for reasoning about persistence and causation
Computational Intelligence
Qualitative probabilities: a normative framework for commonsense reasoning
Qualitative probabilities: a normative framework for commonsense reasoning
Encyclopedia of Artificial Intelligence
Encyclopedia of Artificial Intelligence
Qualitative decision under uncertainty: back to expected utility
Artificial Intelligence
An order of magnitude calculus
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Dominant decisions by argumentation agents
ArgMAS'09 Proceedings of the 6th international conference on Argumentation in Multi-Agent Systems
Using Possibilistic Logic for Modeling Qualitative Decision: ATMS-based Algorithms
Fundamenta Informaticae
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The primary theme of this investigation is a decision theoretic account of conditional ought statements (e.g., "You ought to do A, if C") that rectifies glaring deficiencies in classical deontic logic. The resulting account forms a sound basis for qualitative decision theory, thus providing a framework for qualitative planning under uncertainty. In particular, we show that adding causal relationships (in the form of a single graph) as part of an epistemic state is sufficient to facilitate the analysis of action sequences, their consequences their interaction with observations, their expected utilities and, hence, the synthesis of plans and strategies under uncertainty.