Learning Opportunity Costs in Multi-Robot Market Based Planners

— Direct human control of multi-robot systems is limited by the cognitive ability of humans to coordinate numerous interacting components. In remote environments, such as those encountered during planetary or ocean exploration, a further limit is imposed by communication bandwidth and delay. Market based planning can give humans a higher-level interface to multi-robot systems in these scenarios. Operators provide high level tasks and attach a reward to the achievement of each task. The robots then trade these tasks through a market based mechanism. The challenge for the system designer is to create bidding algorithms for the robots that yield high overall system performance. Opportunity cost provides a nice basis for such bidding algorithms since it encapsulates all the costs and benefits we are interested in. Unfortunately, computing it can be difficult. We propose a method of learning opportunity costs in market based planners. We provide analytic results in simplified scenarios...

• Type: Conference
• Year: 2005
• Where: ICRA
• Authors: Jeff G. Schneider, David Apfelbaum, Drew Bagnell, Reid G. Simmons
• Keywords: Bidding Algorithms, ICRA 2005, Multi-robot Systems, Opportunity Costs, Robotics