Semantic Web policies are general statements defining the behavior of a system that acts on behalf of real users. These policies have various applications ranging from dynamic agent control to advanced access control policies. Although policies attracted a lot of research efforts in recent years, suitable representation and reasoning facilities allowing for reactive policies are not likewise de...

We consider a novel use of mostly-correct reactive policies. In classical planning, reactive policy learning approaches could find good policies from solved trajectories of small problems and such policies have been successfully applied to larger problems of the target domains. Often, due to the inductive nature, the learned reactive policies are mostly correct but commit errors on some portion...

Project scheduling is the part of project management that deals with determining when intime to start (and finish) which activities and with the allocation of scarce resources to theproject activities. In practice, virtually all project managers are confronted with resourcescarceness. In such cases, the Resource-Constrained Project Scheduling Problem (RCPSP)arises. This optimization problem has...

Organized by three distinguished members of the JBS Editorial Board – Marc Bickle, PhD, of the Max Planck Institute of Molecular Cell Biology and Genetics (Germany); Hakim Djaballah, PhD, of Institut Pasteur Korea (South Korea); and Lorenz Martin Mayr, PhD, of AstraZeneca (UK) – this special issue demonstrates how RNAi is enjoying a revival of popularity and is increasingly being applied to dis...

At one time, JEAB editorial policy was perceived by some to consist mainly of dogmatically enforcing a Skinnerian interpretation of all findings reported in the journal. Partly in response to that undeserved reputation, the journal explicitly defined itself as the place to publish research on the behavior of individual organisms, and not as a place that encourages any particular theoretical ori...

We present a planning system for selecting policies in probabilistic planning domains. Our system is based on a variant of approximate policy iteration that combines inductive machine learning and simulation to perform policy improvement. Given a planning domain, the system iteratively improves the best policy found so far until no more improvement is observed or a time limit is exceeded. Thoug...

This paper presents a new method for predicting the values of policies for cloned multiple teleo-reactive robots operating in the context of exogenous events. A teleo-reactive robot behaves autonomously under the control of a policy and is pre-disposed by that policy to achieve some goal. Our approach plans for a set of conjoint robots by focusing upon one representative of them. Simulation res...

In applying reinforcement learning to agents acting in the real world we are often faced with tasks that are non-Markovian in nature. Much work has been done using state estimation algorithms to try to uncover Markovian models of tasks in order to allow the learning of optimal solutions using reinforcement learning. Unfortunately these algorithms which attempt to simultaneously learn a Markov m...

We describe a representation in a high-level transition system for policies that express a reactive behavior for the agent. We consider a target decision component that figures out what to do next and an (online) planning capability to compute the plans needed to reach these targets. Our representation allows one to analyze the flow of executing the given reactive policy, and to determine wheth...