ALICA 2.0 - Domain-Independent Teamwork

2019 ◽  
pp. 264-272
Author(s):  
Stephan Opfer ◽  
Stefan Jakob ◽  
Alexander Jahl ◽  
Kurt Geihs
Keyword(s):  
Domain Independent

Collective Choice and Mutual Knowledge Structures

1998 ◽  
Vol 01 (02n03) ◽  
pp. 221-236 ◽  
Author(s):  
Diana Richards ◽  
Brendan D. McKay ◽  
Whitman A. Richards
Keyword(s):  
Complex System ◽  
Social Choice ◽  
Group Decision ◽  
Knowledge Structure ◽  
Collective Choice ◽  
Sense Data ◽  
Mutual Knowledge ◽  
Interacting Agents ◽  
Aggregation Of Information ◽  
Domain Independent

The conditions under which the aggregation of information from interacting agents results in a stable or an unstable collective outcome is an important puzzle in the study of complex systems. We show that if a complex system of aggregated choice respects a mutual knowledge structure, then the prospects of a stable collective outcome are considerably improved. Our domain-independent results apply to collective choice ranging from perception, where an interpretation of sense data is made by a collection of perceptual modules, to social choice, where a group decision is made from a set of preferences held by individuals.

Heuristics for Domain-Independent Planning: A Survey

2011 ◽  
Vol 135-136 ◽  
pp. 573-577 ◽  
Author(s):  
Rui Shi Liang ◽  
Min Huang
Keyword(s):  
State Space ◽  
Heuristic Search ◽  
Research Work ◽  
Future Research ◽  
Survey Analysis ◽  
Work Related ◽  
State Space Search ◽  
Survey Results ◽  
Domain Independent ◽  
Intense Research

Increasing interest has been devoted to Planning as Heuristic Search over the years. Intense research has focused on deriving fast and accurate heuristics for domain-independent planning. This paper reports on an extensive survey and analysis of research work related to heuristic derivation techniques for state space search. Survey results reveal that heuristic techniques have been extensively applied in many efficient planners and result in impressive performances. We extend the survey analysis to suggest promising avenues for future research in heuristic derivation and heuristic search techniques.

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