Towards a Generic Architecture for Multi-Vehicle Autonomy (2008)
Malcolm Strens, Spiros Kapetanakis, Jeremy Baxter
We describe the motivation for a programme of research “MP012: Software Architecture for Hybrid Decision Making ” that is being undertaken in SEAS DTC Year 2. There are many ways to endow an...
Relational Spatial Features in Reinforcement Learning of Multi-Agent Search Strategies (2008)
We address a partially observable twodimensional multi-pursuer evader searching task. Belief compression for each pursuer is achieved using spatial basis functions to obtain a set of relational...
Algorithms for Distributed Exploration (2008)
Thomas Walker, Daniel Kudenko, Malcolm Strens
In this paper we propose algorithms for a set of problems where a distributed team of agents tries to compile a global map of the environment from local observations. We focus on two approaches: one...
Learning to Coordinate Using Commitment Sequences in Cooperative Multi-Agent Systems (2007)
Spiros Kapetanakis, Daniel Kudenko, Malcolm Strens
We report on an investigation of the learning of coordination in cooperative multi-agent systems. Specifically, we study solutions that are applicable to independent agents i.e. agents that do not...
Efficient Hierarchical MCMC for Policy Search (2007)
Many inference and optimization tasks in machine learning can be solved by sampling approaches such as Markov Chain Monte Carlo (MCMC) and simulated annealing. These methods can be slow if a single...
N.: Combining planning with reinforcement learning for multi-robot task allocation (2005)
Malcolm Strens, Neil Windelinckx
Abstract. We describe an approach to the multi-robot task allocation (MRTA) problem in which a group of robots must perform tasks that arise continuously, at arbitrary locations across a large space....
Learning to coordinate using commitment sequences in cooperative multiagent-systems (2003)
Spiros Kapetanakis, Daniel Kudenko, Malcolm Strens
We report on an investigation of the learning of coordination in cooperative multiagent systems. Specifically, we study solutions that are applicable to independent agents, i.e., agents that do not...
A Bayesian framework for reinforcement learning (2000)
The reinforcement learning problem can be decomposed into two parallel types of inference: (i) estimating the parameters of a model for the underlying process; (ii) determining behavior which...
Representation of uncertainty in spatial target tracking (1998)
Tim Baker, Malcolm Strens, Dera Farnborough
This paper presents a novel representation of information within tracking applications, called the Spatial Probability Density Function (PDF) representation. This representation allows a level of...