Autonomous agents in real-world environments may encounter undesirable outcomes or negative side effects (NSEs) when working collaboratively alongside other agents. We frame the challenge of minimizing NSEs in a multi...
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ISBN:
(纸本)9798400704864
Autonomous agents in real-world environments may encounter undesirable outcomes or negative side effects (NSEs) when working collaboratively alongside other agents. We frame the challenge of minimizing NSEs in a multi-agent setting as a lexicographic decentralized Markov decision process in which we assume independence of rewards and transitions with respect to the primary assigned tasks, but allowing negative side effects to create a form of dependence among the agents. We present a lexicographic Q-learning approach to mitigate the NSEs using human feedback models while maintaining near-optimality with respect to the assigned tasks-up to some given slack. Our empirical evaluation across two domains demonstrates that our collaborative approach effectively mitigates NSEs, outperforming non-collaborative methods.
In scenarios with numerous emergencies that arise and require the assistance of various rescue units (e.g., medical, fire, & police forces), the rescue units would ideally be allocated quickly and distributedly wh...
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ISBN:
(纸本)9781450394321
In scenarios with numerous emergencies that arise and require the assistance of various rescue units (e.g., medical, fire, & police forces), the rescue units would ideally be allocated quickly and distributedly while aiming to minimize casualties. This is one of many examples of distributed settings with service providers (the rescue units) and service requesters (the emergencies) which we termservice oriented settings. Allocating the service providers in a distributed manner while aiming for a global optimum is hard to model, let alone achieve, using the existing distributedconstraintoptimization Problem (DCOP) framework. Hence, the need for a novel approach and corresponding algorithms. We present the Service Oriented Multi-Agent optimization Problem (SOMAOP), a new framework that overcomes the shortcomings of DCOP in service oriented settings. We evaluate the framework using various algorithms based on auctions and matching algorithms (e.g., Gale Shapely). We empirically show that algorithms based on repeated auctions converge to a high quality solution very fast, while repeated matching problems converge slower, but produce higher quality solutions. We demonstrate the advantages of our approach over standard incomplete DCOP algorithms and a greedy centralized algorithm.
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