We explore techniques for maintaining consistency in reasoning when employing dynamic hierarchical task decompositions. In particular, we consider the difficulty of maintaining co...
— This paper is about collision avoidance of crowd robots. For this purpose a model of potential field is proposed. This potential field, generated by a neural network, is uniq...
We present a decentralized, asynchronous market protocol for allocating and scheduling tasks among agents that contend for scarce resources, constrained by a hierarchical task dep...
This paper presents an algorithm for extract ing propositions from trained neural networks. The algorithm is a decompositional approach which can be applied to any neural networ...
Stochastic games generalize Markov decision processes MDPs to a multiagent setting by allowing the state transitions to depend jointly on all player actions, and having rewards de...
Michael J. Kearns, Yishay Mansour, Satinder P. Sin...