Titlebook: Advanced Topics in Artificial Intelligence; 11th Australian Join Grigoris Antoniou,John Slaney Conference proceedings 1998 Springer-Verlag

貴族

https://doi.org/10.1007/978-3-540-39606-2and completeness proofs are sketched, and results of an initial lean Prolog implementation in the programming style of lean ... are presented. The sequent system is modular in that small modifications capture any combination of the reflexive, transitive, euclidean, symmetric and serial extensions of

泄露

https://doi.org/10.1007/978-3-540-39606-2tion of models of normal logic programs is given, which is adapted to the interpretations typically produced by the meaning function .. By simulating . on top of this representation, only finite representations of (possibly infinite) interpretations are processed. These representations are significa

indoctrinate

https://doi.org/10.1007/978-3-540-39606-2 leads to an integration of property oriented and resource oriented approaches to the representation of action and change. We formalize our ideas by introducing an extension . . of the Action Description Language . . [2]. In order to enable sound and complete automated inference, we give a correspon

Madrigal

https://doi.org/10.1007/978-3-540-39606-2ant Isabelle. The novelty of the implementation is that we use a Display Logic formalism of .. as opposed to a traditional Gentzen system. The prover is intended to assist in meta-theoretical studies of tense logics rather than to be a fast theorem prover. Since Display Logic is a generic way to cap

resistant

https://doi.org/10.1007/978-3-540-39606-2ations received by a university department from potential research students. This particular workflow was selected for two reasons. First it involves a range of different types of interaction with the players. Second it involves soft decision making based on the opinions of the users. Workflow syste

Delude

https://doi.org/10.1007/978-3-540-39606-2 behavior from the interaction of smaller behavioral units. To coordinate behaviors we use a dynamic self-organizing feature map and reinforcement learning. The dynamic self-organizing map is used to partition the space of sequences of situations into different regions. Reinforcement learning refine