(Abstraction of State-Action Pairs) frame-work, which extends and unifies past work on do-main abstractions by holistically aggregating both states and state-action pairs – ASAP uncovers a much larger number of symmetries in a given do-main. Second, we propose ASAP-UCT, which im-plements ASAP

network of such switches. These models motivate the Packet-Pair rate probing technique, and a provably stable rate-based flow control scheme. A Kalman state estimator is derived from discrete-time state space analysis, but there are difficulties in using the estimator in practice. These difficulties

under π, which we assume exists and is independent of s 0 for all policies. In the average reward formulation, the value of a state-action pair given a policy is defined as The second formulation we cover is that in which there is a designated start state s 0 , and we care only about the long

This paper describes a system for the evolution and co-evolution of virtual creatures that compete in physically simulated three-dimensional worlds. Pairs of individuals enter one-on-one contests in which they contend to gain control of a common resource. The winners receive higher relative fitness

Robots that works in a dynamic environment must possess, the ability to autonomously cope with the changes in the environment. This paper proposes an approach to predict changes in the state and actions of robots. Further, this approach attempts to ap-ply predicted future actions to current actions

We present a concept to use off-line learning approaches to achieve on-line learning of cooperative behavior of agents and instantiate this concept for evolutionary learning with agents based on prototype situation-action-pairs and the nearest-neighbor rule. For such an agent model also modeling

Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics

of the above examples and interprets their symplectic structures in terms of the inter-section pairing on the surface. Our setup is as follows. Consider a closed oriented topological surface S with fundamental group rt and let G be a connected Lie group. The space Hom(n, G) consisting of representations rr + G

. Action bank is comprised of many individual action detectors sampled broadly in semantic space as well as viewpoint space. Our representation is constructed to be semantically rich and even when paired with simple linear SVM classifiers is capable of highly discriminative performance. We have tested

constraints, to perform corresponding actions and to control their execution in real-time, while being reactive to possible events. The general architecture is composed of three levels: a decision level, an execution level and a functional level. The later is composed of modules that embed the functions