Reasoning about action I: a possible worlds approach
Artificial Intelligence
Nonmonotonic reasoning, preferential models and cumulative logics
Artificial Intelligence
Artificial Intelligence
Modelling intertia in action languages
PRICAI '96 Selected Papers from the Workshop on Reasoning with Incomplete and Changing Information and on Inducing Complex Representations: Learning and Reasoning with Complex Representations
Preferential semantics for causal systems
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
A causal theory of ramifications and qualifications
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Embracing causality in specifying the indirect effects of actions
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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A unifying semantic framework for different reasoning approaches provides an ideal tool to compare these competing alternatives A historic example is Kripke's possible world semantics that provided a unifying framework for different systems of modal logic. More recently, Shoham's work on preferential semantics similarly provided a much needed framework to uniformly represent and compare a variety of nonmonotonic logics (including some logics of action). The present work develops a novel type of semantics for a particular causal approach to reasoning about action. The basic idea is to abandon the standard state-space of possible worlds and consider instead a larger set of possibilities -- a hyper-space -- tracing the effects of auctions (including indirect effects) with the states in the hyper-space. Intuitively, the purpose of these hyper-states is to supply extra context to record the process of causality.