What is recorded here?
Presented a unified POMDP treatment for choosing actions under partial observability, including offline solution methods and extraction of finite-memory controllers in applicable cases.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The record is the exact three-author 1998 Artificial Intelligence paper. Optimality and controller claims remain within the finite models and algorithms analyzed; it does not absorb all POMDP theory, reinforcement learning or later approximations.
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People and systems
Leslie Pack Kaelbling; Michael L. Littman; Anthony R. Cassandra
The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.
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Documented countries / regions: United States
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English catalogue narrative
Kaelbling, Littman and Cassandra: planning in partially observable stochastic domains
Presented a unified POMDP treatment for choosing actions under partial observability, including offline solution methods and extraction of finite-memory controllers in applicable cases.
Scientific scope
The record is the exact three-author 1998 Artificial Intelligence paper. Optimality and controller claims remain within the finite models and algorithms analyzed; it does not absorb all POMDP theory, reinforcement learning or later approximations.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Leslie Pack Kaelbling; Michael L. Littman; Anthony R. Cassandra
The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.