We investigate the problem of learning action effects in partially observable STRIPS planning domains. Our approach is based on a voted kernel perceptron learning model, where act...
Abstract. Approximate dynamic programming offers a new modeling and algorithmic strategy for complex problems such as rail operations. Problems in rail operations are often modeled...
Chip multi-processors (CMP) are rapidly emerging as an important design paradigm for both high performance and embedded processors. These machines provide an important performance...
Alex Settle, Dan Connors, Enric Gibert, Antonio Go...
Preferences in constraint problems are common but significant in many real world applications. In this paper, we extend our conditional and composite CSP (CCCSP) framework, managi...
Abstract. This paper extends dynamic symbolic execution to distributed and concurrent systems. Dynamic symbolic execution can be used in software testing to systematically identify...
Andreas Griesmayer, Bernhard K. Aichernig, Einar B...