Multi-agent systems (MAS) provide a promising technology for addressing problems such as search and rescue missions, mine sweeping, and surveillance. These problems are a form of the computationally intractable Multi-Depot Traveling Salesman Problem (MDTSP). We propose a novel market-based approach, called Market-based Approach with Look-ahead Agents (MALA), to address the problem. In MALA, agents use look ahead to optimize their behavior. Each agent plans a preferred, reward-maximizing tour

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... itself using our proposed algorithm which is based on the Universal TSP algorithm. The agent then uses the preferred tour to evaluate potential trades with other agents in linear time -a necessary prerequisite for scalability of market-based approach. We use simulations in a two dimensional world to study the performance of MALA and compare it with O-contracts and TraderBots, respectively, a centralized approach and a distributed approach. Experiments suggest that MALA efficiently scales to thousands of tasks and hundreds of agents in terms of both computation and communication complexity, while delivering relatively good-quality but approximate solutions. *