The ability to transfer knowledge gained in previous tasks into new contexts is one of the most important mechanisms of human learning. Despite this, adapting autonomous behavior to be reused in partially similar settings is still an open problem in current robotics research. In this paper, we take a small step in this direction and propose a generic framework for learning transferable motion p...

This thesis proposes the MAP-REDUCE framework, a programmable framework, that can be used to construct enforcement mechanisms of different security policies. The framework is based on the idea of secure multiexecution in which multiple copies of the controlled program are executed. In order to construct an enforcement mechanism of a policy, users have to write a MAP program and a REDUCE program...

This paper describes the use of a dynamical simulation model to evaluate various management policies in a two-stage production systems. Such a simulation-based evaluation is applied to the situation of GrandIlet (Reunion Island) where 51 pig farms must supply a collective treatment plant (CTP) with pig slurry. Based on actual data and stakeholders’ preferences, three policies are tested: a plan...

As autonomous systems become more common in our lives, the issue of verifying that they behave as intended and that their design policies are correct becomes more important. This thesis aims to build foundations for such a verification capability for policies with a reactive behavior, with a focus on combining the representation power of action languages with model checking techniques.

Humans are used to move in environments, where other humans are present. Due to this fact it is easier for humans to predict the path of a robot, if the robot navigates human-like. In this thesis we present a reactive navigation method to create human-like paths to navigate mobile robots. There are two basic approaches for social navigation of mobile robots. The first group are reactive approac...

The Semantic Web envisions a distributed environment with well-defined data that can be understood and used by machines in order to, among others, allow intelligent agents to automatically perform tasks on our behalf. In the past, different Semantic Web policy languages have also been described as a powerful means to describe a system’s behavior by defining statements about how the system must ...

We consider the problem of learning for planning, where knowledge acquired while planning is reused to plan faster in new problem instances. For robotic tasks, among others, plan execution can be captured as a sequence of visual images. For such domains, we propose to use deep neural networks in learning for planning, based on learning a reactive policy that imitates execution traces produced b...

The State is not always able to proactively improve environmental policies. Eventually, policy improvements are a result of disasters that expose pre-existing problems. This situation reflected in the state Minas Gerais (Brazil), where, after failures Fundão and B1 tailings dam, Mariana Brumadinho, several problems dam safety emergency policies were exposed. study had twofold objective: 1) iden...

Policy Evaluation for Reactive Agents Krysia Broda and Christopher John Hogger Department of Computing, Imperial College London South Kensington Campus, London SW7 2AZ UK {kb, cjh}@doc.ic.ac.uk Abstract. This paper describes a method for constructing and evaluating teleo-reactive policies for one or more agents, based upon discountedreward evaluation of policy-restricted subgraphs of complete s...