Artificial Intelligence
A logic to reason about likelihood
Artificial Intelligence
A logic for reasoning about probabilities
Information and Computation - Selections from 1988 IEEE symposium on logic in computer science
Likelihood, probability, and knowledge
Computational Intelligence
Reasoning about knowledge and probability
Journal of the ACM (JACM)
Reasoning about knowledge
An algorithm for probabilistic planning
Artificial Intelligence - Special volume on planning and scheduling
Modal logic
Epistemic Logic for AI and Computer Science
Epistemic Logic for AI and Computer Science
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Errata: “The relationship between knowledge, belief, and certainty”
Annals of Mathematics and Artificial Intelligence
An Open-Ended Finite Domain Constraint Solver
PLILP '97 Proceedings of the9th International Symposium on Programming Languages: Implementations, Logics, and Programs: Including a Special Trach on Declarative Programming Languages in Education
Probabilistic Models for Agent's Beliefs and Decisions
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Reasoning about Uncertainty
Logical implementation of uncertain agents
EPIA'05 Proceedings of the 12th Portuguese conference on Progress in Artificial Intelligence
Verification of epistemic properties in probabilistic multi-agent systems
MATES'09 Proceedings of the 7th German conference on Multiagent system technologies
Combined model checking for temporal, probabilistic, and real-time logics
Theoretical Computer Science
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Logical formalisation of agent behaviour is desirable, not only in order to provide a clear semantics of agent-based systems, but also to provide the foundation for sophisticated reasoning techniques to be used on, and by, the agents themselves. The possible worlds semantics offered by modal logic has proved to be a successful framework in which to model mental attitudes of agents such as beliefs, desires and intentions. The most popular choices for modeling the informational attitudes involves annotating the agent with an S5-like logic for knowledge, or a KD45-like logic for belief. However, using these logics in their standard form, an agent cannot distinguish situations in which the evidence for a certain fact is 'equally distributed' over its alternatives, from situations in which there is only one, almost negligible, counterexample to a 'fact'. Probabilistic modal logics are a way to address this, but they easily end up being both computationally and conceptually complex, for example often lacking the property of compactness. In this paper, we propose a probabilistic modal logic P"FKD45, in which the probabilities of the possible worlds range over a finite domain of values, while still allowing the agent to reason about infinitely many options. In this way, the logic remains compact, implying that the agent still has to consider only finitely many possibilities for probability distributions during a reasoning task. We demonstrate a sound, compact and complete axiomatization for P"FKD45 and show that it has several appealing features. Then, we discuss an implemented decision procedure for the logic, and provide a small example.