studied using phenomenology, this paper concludes with a model of adaptation in polycontextual work environments.

exploration of the possibilities of future work in the area. Summary 1. Overview This thesis presents an overview of trust as a social phenomenon and discusses it formally. It argues that trust is: • A means for understanding and adapting to the complexity of the environment. • A means of providing added

Abstract. Reinforcement learning policies face the exploration versus exploitation dilemma, i.e. the search for a balance between exploring the environment to find profitable actions while taking the empirically best action as often as possible. A popular measure of a policy’s success in addressing

We explore the performance of reliable data communication in mobile computing environments. Motion across wireless cell boundaries causes increased delays and packet losses while the network learns how to route data to a host's new location. Reliable transport protocols like TCP interpret

in the mean or variability in any variable that defines a system's environment. Moreover, the care with which they assessed the broader adaptation literature in their approach to climate issues means that it is sufficient for present purposes simply to review their work with a careful eye toward

, and the learning task can be viewed as an optimization problem in a noisy environment. Previous studies have shown that genetic algorithms can perform effectively in the presence of noise. This work ex-plores in detail the tradeoffs between the amount of effort spent on evaluating each structure and the number

In this paper, we explore the use of alternative mutation strategies as a means of increasing diversity so that the GA can track the optimum of a changing environment. This paper contrasts three different strategies: the Standard GA using a constant level of mutation, a mechanism called Random

of previous RRT work, the waypoint cache and adaptive cost penalty search, which improve replanning efficiency and the quality of generated paths. ERRT is successfully applied to a real-time multi-robot system. Results demonstrate that ERRT is significantly more efficient for replanning than a basic RRT

, it tends to produce models that adapt to their task environment about as fast as humans adapt. (This congruence with human behavior is in marked contrast to other popular ways of computing the utility of alternative choices; for example, Reinforcement Learning or most Connectionist learning methods.) We

in the somewhat more antagonistic situation of bargaining to split a fixed payoff, or attempting to escape the undesirable but compelling equilibrium of a Prisoner's Dilemma. As time progresses, the players adapt their strategies, perhaps incorporating randomness in their decision rules, to suit