New domains are emerging that impose new requirements for teamwork, where current teamwork infrastructure is inadequate. This paper focuses on Distributed Constraint Optimization (DCOP) for role allocation, as DCOP offers the key advantages of distributedness and a rich representational language which can consider costs/utilities of tasks. In particular, we propose a novel DCOP algorithm called LA-DCOP (Low communication Approximate DCOP). Our empirical results show that LA-DCOP favorably compares with state of the art heuristics (such as DSA) in a synthetic scenario and in two simulations of realistic domains (i.e., search and rescue and UAV surveillance).
Allocating Roles in Extreme Team
FARINELLI, Alessandro;
2004-01-01
Abstract
New domains are emerging that impose new requirements for teamwork, where current teamwork infrastructure is inadequate. This paper focuses on Distributed Constraint Optimization (DCOP) for role allocation, as DCOP offers the key advantages of distributedness and a rich representational language which can consider costs/utilities of tasks. In particular, we propose a novel DCOP algorithm called LA-DCOP (Low communication Approximate DCOP). Our empirical results show that LA-DCOP favorably compares with state of the art heuristics (such as DSA) in a synthetic scenario and in two simulations of realistic domains (i.e., search and rescue and UAV surveillance).
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