We investigate search problems under risk in statespace graphs, with the aim of finding optimal paths for risk-averse agents. We consider problems where uncertainty is due to the...
Typically, Markov decision problems (MDPs) assume a single action is executed per decision epoch, but in the real world one may frequently execute certain actions in parallel. Thi...
In order to scale with modern processors, planning algorithms must become multi-threaded. In this paper, we present parallel shared-memory algorithms for two problems that underli...
This paper describes a speciation method that allows an evolutionary process to learn several robot behaviors using a single execution. Species are created in behavioral space in ...
Embedded systems have an ever-increasing need for optimizing compilers to produce high quality codes with a limited general purpose register set. Either memory or registers are use...