Learning Opportunity Costs in Multi-Robot Market Based Planners

Jeff Schneider, David Apfelbaum, J. Andrew (Drew) Bagnell, and Reid Simmons
April, 2005.

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Direct human control of multi-robot systems is limited by the cognitive ability of humans to coordinate nu- merous 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 achieve- ment 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 and empirical results on our FIRE simulator, which focuses on exploration of Mars by multiple, heterogeneous rovers. Index Terms— Market-based planning, learning, opportu- nity cost, multi-robot systems.


Text Reference
Jeff Schneider, David Apfelbaum, J. Andrew (Drew) Bagnell, and Reid Simmons, "Learning Opportunity Costs in Multi-Robot Market Based Planners," April, 2005.

BibTeX Reference
   author = "Jeff Schneider and David Apfelbaum and J. Andrew (Drew) Bagnell and Reid Simmons",
   title = "Learning Opportunity Costs in Multi-Robot Market Based Planners",
   booktitle = "",
   month = "April",
   year = "2005",
   number= "CMU-RI-TR-",