We present CGO-AS, a generalized Ant System (AS) implemented in the framework of Cooperative Group Optimization (CGO), to show the leveraged optimization with a mixed individual and social learning. Ant colony is a simple yet efficient natural system for understanding the effects of primary intelligence on optimization. However, existing AS algorithms are mostly focusing on their capability of using social heuristic cues while ignoring their individual learning. CGO can integrate the advantages of a cooperative group and a low-level algorithm portfolio design, and the agents of CGO can explore both individual and social search. In CGO-AS, each ant (agent) is added with an individual memory, and is implemented with a novel search strategy to use individual and social cues in a controlled proportion.
Hao, Jianye (Massachusetts Institute of Technology) | Huang, Dongping (South China University of Technology) | Cai, Yi (South China University of Technology) | Leung, Ho-fung (The Chinese University of Hong Kong)
The problem of coordination in cooperative multiagent systems has been widely studied in the literature. In practical complex environments, the interactions among agents are usually regulated by their underlying network topology, which, however, has not been taken into consideration in previous work. To this end, we firstly investigate the multiagent coordination problems in cooperative environments under the networked social learning framework focusing on two representative topologies: the small-world and the scale-free network. We consider a population of agents where each agent interacts with another agent randomly chosen from its neighborhood in each round. Each agent learns its policy through repeated interactions with its neighbors via social learning. It is not clear a priori if all agents can learn a consistent optimal coordination policy and what kind of impact different topology parameters could have on the learning performance of agents. We distinguish two types of learners: individual action learner and joint action learner. The learning performances of both learners are evaluated extensively in different cooperative games, and the influence of different factors on the learning performance of agents is investigated and analyzed as well.
Coordination in cooperative multiagent systems is an important problem in multiagent learning literature. In practical complex environments, the interactions between agents can be sparse, and each agent's interacting partners may change frequently and randomly. To this end, we investigate the multiagent coordination problems in cooperative environments under the social learning framework. We consider a large population of agents where each agent interacts with another agent randomly chosen from the population in each round. Each agent learns its policy through repeated interactions with the rest of agents via social learning. It is not clear a priori if all agents can learn a consistent optimal coordination policy in such a situation. We distinguish two types of learners: individual action learner and joint action learner. The learning performance of both learners are evaluated under a number of challenging cooperative games, and the influence of the information sharing degree on the learning performance is investigated as well.
We provide an algorithm to compute near-optimal strategies for the Cultaptation social learning game. We show that the strategies produced by our algorithm are near-optimal, both in their expected utility and their expected reproductive success. We show how our algorithm can be used to provide insight into evolutionary conditions under which learning is best done by copying others, versus the conditions under which learning is best done by trial-and-error.
This paper examines the value of innovation within a culture by looking at "innovate" moves in the Cultaptation Project's social learning game (Boyd et al. 2008). We produce a mathematical model of a simplified version of this game, and produce analytic methods for determining optimal innovation behavior in this game. In particular, we provide a formula for determining when one should stop innovating and start exploiting one's accumulated knowledge. We create an agent for playing the social learning game based on these results, and in our experiments, the agent exhibited near-optimal behavior.