Systems are becoming exceedingly complex to manage. As such, there is an increasing trend towards developing systems that are self-managing. Policy-based infrastructures have been used to provide a limited degree of automation, by associating actions to system-events. In the context of self-managing systems, the existing policy-specification model fails to capture the following: a) The impact o...

this book makes abundantly clear the sustained freshness of snyder path-breaking and dauntingly original work, which has long set the agenda for the systematic explanation of foreign policy decisions. thevery thoughtful essays prepared for the present volume demonstrate dramatically the continued centrality of the issues snyder and his associates raised more than 40 years ago. prescient in its ...

this article explores the intricate interrelationships between discourses on identity and themultiple processes associated with increasing globalization in the modern age. globalization is notonly often exclusively associated with worldwide economic integration and the emergence of aborderless global market but also involves sweeping changes on the social, cultural and politicaldomains. further...

In the Bayesian approach to sequential decision making, exact calculation of the (subjective) utility is intractable. This extends to most special cases of interest, such as reinforcement learning problems. While utility bounds are known to exist for this problem, so far none of them were particularly tight. In this paper, we show how to efficiently calculate a lower bound, which corresponds to...

The purpose of reinforcement learning system is to learn an optimal policy in general. However, in 2players games such as the othello game, it is important to acquire a penalty avoiding policy. In this paper, we are focused on formation of penalty avoiding policies based on the Penalty Avoiding Rational Policy Making algorithm [2]. In applying it to large-scale problems, we are confronted with ...

We use simulated soccer to study multiagent learning. Each team's players (agents) share action set and policy but may behave diierently due to position-dependent inputs. All agents making up a team are rewarded or punished collectively in case of goals. We conduct simulations with varying team sizes, and compare two learning algorithms: TD-Q learning with linear neural networks (TD-Q) and Prob...