Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/41119
Title: On learning to coordinate random bits help, insightful normal forms, and competency isomorphisms
Authors: Case, J.
Jain, S. 
Montagna, F.
Simi, G.
Sorbi, A.
Issue Date: 2003
Source: Case, J.,Jain, S.,Montagna, F.,Simi, G.,Sorbi, A. (2003). On learning to coordinate random bits help, insightful normal forms, and competency isomorphisms. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) 2777 : 699-713. ScholarBank@NUS Repository.
Abstract: A mere bounded number of random bits judiciously employed by a probabilistically correct algorithmic coordinator is shown to increase the power of learning to coordinate compared to deterministic algorithmic coordinators. Furthermore, these probabilistic algorithmic coordinators are provably not characterized in power by teams of deterministic ones. An insightful, enumeration technique based, normal form characterization of the classes that are learnable by total computable coordinators is given. These normal forms are for insight only since it is shown that the complexity of the normal form of a total computable coordinator can be infeasible compared to the original coordinator. Montagna and Osherson showed that the competence class of a total coordinator cannot be strictly improved by another total coordinator. It is shown in the present paper that the competencies of any two total coordinators are the same modulo isomorphism. Furthermore, a completely effective, index set version of this competency isomorphism result is given, where all the coordinators are total computable.
Source Title: Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
URI: http://scholarbank.nus.edu.sg/handle/10635/41119
ISSN: 03029743
Appears in Collections:Staff Publications

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