Fitness function shaping in multiagent cooperative coevolutionary algorithms

Colby, Mitchell; Tumer, Kagan

doi:10.1007/s10458-015-9318-0

Cited by 9 publications

(4 citation statements)

References 39 publications

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“…For example, an agent might be a "good" agent, but the agents it collaborates with are "bad," and the objective is not reached. In this case, the good agent may be perceived as bad) [48,49]…”

Section: Cooperative Coevolutionary Algorithms Cooperative Coevolutimentioning

confidence: 99%

“…This reduces noise in the feedback signal, meaning that difference evaluations are highly sensitive to the actions of an individual agent. In addition to the theoretical properties of alignment and sensitivity, difference evaluations have been proven to increase the probability of finding optimal solutions in cases where the optimal Nash equilibrium is deceptive [49]. Difference evaluations do not affect the location, number, or relative ordering of Nash equilibrium [49,53].…”

Section: Cooperative Coevolutionary Algorithms Cooperative Coevolutimentioning

confidence: 99%

“…In addition to the theoretical properties of alignment and sensitivity, difference evaluations have been proven to increase the probability of finding optimal solutions in cases where the optimal Nash equilibrium is deceptive [49]. Difference evaluations do not affect the location, number, or relative ordering of Nash equilibrium [49,53]. A deceptive Nash equilibrium is one in which the payoff drops significantly if one agent deviates.…”

Section: Cooperative Coevolutionary Algorithms Cooperative Coevolutimentioning

confidence: 99%

“….3.2.2 Difference Evaluation FunctionTheory. The agentspecific difference evaluation function is defined as[49][50][51] …”

mentioning

confidence: 99%

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Design of Complex Engineered Systems Using Multi-Agent Coordination

Zurita

Colby

Tumer

et al. 2017

Journal of Computing and Information Science in Engineering

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In complex engineering systems, complexity may arise by design, or as a by-product of the system's operation. In either case, the cause of complexity is the same: the unpredictable manner in which interactions among components modify system behavior. Traditionally, two different approaches are used to handle such complexity: (i) a centralized design approach where the impacts of all potential system states and behaviors resulting from design decisions must be accurately modeled and (ii) an approach based on externally legislating design decisions, which avoid such difficulties, but at the cost of expensive external mechanisms to determine trade-offs among competing design decisions. Our approach is a hybrid of the two approaches, providing a method in which decisions can be reconciled without the need for either detailed interaction models or external mechanisms. A key insight of this approach is that complex system design, undertaken with respect to a variety of design objectives, is fundamentally similar to the multi-agent coordination problem, where component decisions and their interactions lead to global behavior. The results of this paper demonstrate that a team of autonomous agents using a cooperative coevolutionary algorithm (CCEA) can effectively design a complex engineered system. This paper uses a system model of a Formula SAE racing vehicle to illustrate and simulate the methods and potential results. By designing complex systems with a multi-agent coordination approach, a design methodology can be developed to reduce design uncertainty and provide mechanisms through which the system level impact of decisions can be estimated without explicitly modeling such interactions.

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Section: Cooperative Coevolutionary Algorithms Cooperative Coevolutimentioning

confidence: 99%