Handbook of Temporal Reasoning in Artificial Intelligence (Foundations of Artificial Intelligence)

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Don't have an account? Sign in via your Institution. Sign in with your library card. Search within In This Article 1. The Turing Test 1. The Imitation Game 1. Objections to the Test 1. Fiendish Experts 2. The Chinese Room Argument 2. The Argument 2. The Part-Of Principle 2. The Chinese Gym 2. Wittgenstein and the CRA 3. Computers and Computers 3. Narrow versus Wide Mechanism 3. Narrow Mechanism and the Foundations of AI 3. Hyper-Nets and the Chinese Gym 3.

Voices 2015 Spatial Temporal Reasoning Over Play-Scripts for Artificially Intelligent Characters

Wide Mechanism and the Finiteness of the Universe 4. The Futurists 4. Singularitarianism and the Future of AI 5.

Behavioral Modeling and Decision-making in AI

Kurzweil's Law 5. Objections to Kurzweil's Singularitarianism 5. Artificial Intelligence or Make-Believe Intelligence? Conclusion References Notes. Related Content Functionalism Cognitive Science. Abstract and Keywords In this article the central philosophical issues concerning human-level artificial intelligence AI are presented. Jack Copeland B. Brajnik and D. Clancy, Focusing qualitative simulation using temporal logic: theoretical foundations, in [52] pp. Brusoni, L. Console, P. Terenziani and B. CrossRef Google Scholar. Cervesato, L. Chittaro and A. Montanari, A modal calculus of partially ordered events in a logic programming framework, in: Proc.

Cervesato, M.

Artificial Intelligence | Faculty of Engineering | Imperial College London

Franceschet and A. Montanari, A hierarchy of modal event calculi: expressiveness and complexity, in: Proc. Montanari, Modal event calculi with preconditions, in: Proc. Montanari, Event calculi with explicit quantifiers, in: Proc.


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Extended and revised version of I. Montanari, The complexity of model checking in modal event calculi with quantifiers, in: Proc. Montanari, A guided tour through some extensions of the Event Calculus, Computational Intelligence 16 2 Cervesato and A. Montanari, A general modal framework for the event calculus and its skeptical and credulous variants, Journal of Logic Programming 38 2 Extended and revised version of A.

Montanari, L. Chittaro and I. Cervesato, A general modal framework for the event calculus and its skeptical and credulous variants, in: Proc. Chapman, Planning for conjunctive goals, Artificial Intelligence 32 Cheeseman, B. Kanefsky and W. Taylor, Where the really hard problems are, in: Proc. Chittaro and C. Combi, Temporal indeterminacy in deductive databases: an approach based on the event calculus, in: Proc. Combi, Reasoning about events with imprecise location and multiple granularities, in: Proc.

Combi, Abstraction on clinical data sequences: an object-oriented data model and a query language based on the Event Calculus, Artificial Intelligence in Medicine Journal 17 3 Chittaro, C.

Schedule and Readings

Combi, E. Cervesato, A. Cervesato, F. Antonini-Canterin, G. Nicolosi and D. Zanuttini, Specifying and representing temporal abstractions of clinical data by a query language based on the Event Calculus, in: Proc. Chittaro and M. Chittaro, H. Hamilton, S.

Temporal representation and reasoning in artificial intelligence: Issues and approaches

Goodwin and A. Montanari eds. Rzevski, J. Pastor and R. Adey Elsevier Applied Science, Amsterdam, pp. Montanari, Efficient temporal reasoning in the cached Event Calculus, Computational Intelligence 12 3 MathSciNet Google Scholar. Montanari, Trends in temporal representation and reasoning, The Knowledge Engineering Review 11 3 Montanari and A. Provetti, skeptical and credulous event calculi for supporting modal queries, in: Proc. Ciapessoni, E. Corsetti, A. Montanari and P. San Pietro, Embedding time granularity in a logical specification language for synchronous real-time systems, Science of Computer Programming 20 Combi and Y.

Shahar eds. Console, D.

https://noncdehoho.ga Torasso, On the relationship between abduction and deduction, Journal of Logic and Computation 1 5 Cooper, The computational complexity of probabilistic inference using Bayesian belief networks, Artificial Intelligence 42 Montanari and E. Ratto, Dealing with different time granularities in formal speci-fications of real-time systems, The Journal of Real-Time Systems 3 2 Crawford and D. Etherington, Formalizing reasoning about change: A qualitative reasoning approach, in: Proc. Dean and M. Reasoning about partially ordered events, Artificial Intelligence 36 Dean and D.

Mc Dermott, Temporal data base management, Artificial Intelligence 32 Dechter, I. Meiri and J.

Pearl, Temporal constraint networks, Artificial Intelligence 49 Denecker, L. Missiaen and M. Bruynooghe, Temporal reasoning with abductive event calculus, in: Proc. Deville and P. Doherty, Reasoning about action and change using occlusion, in: Proc. Doherty and W. Lukaszewicz, Circumscribing features and fluents, in: Proc.