References of "Spaan, M. T. J"
     in
Bookmark and Share    
Full Text
Peer Reviewed
See detailNon-communicative multi-robot coordination in dynamic environments
Kok, J. R.; Spaan, M. T. J.; Vlassis, Nikos UL

in Robotics & Autonomous Systems (2005), 50(2-3), 99-114

Within a group of cooperating agents the decision making of an individual agent depends on the actions of the other agents. In dynamic environments, these dependencies will change rapidly as a result of ... [more ▼]

Within a group of cooperating agents the decision making of an individual agent depends on the actions of the other agents. In dynamic environments, these dependencies will change rapidly as a result of the continuously changing state. Via a context-specific decomposition of the problem into smaller subproblems, coordination graphs offer scalable solutions to the problem of multiagent decision making. In this work, we apply coordination graphs to a continuous (robotic) domain by assigning roles to the agents and then coordinating the different roles. Moreover, we demonstrate that, with some additional assumptions, an agent can predict the actions of the other agents, rendering communication superfluous. We have successfully implemented the proposed method into our UvA Trilearn simulated robot soccer team which won the RoboCup-2003 World Championship in Padova, Italy. (C) 2004 Elsevier B.V. All rights reserved. [less ▲]

Detailed reference viewed: 50 (0 UL)
Full Text
Peer Reviewed
See detailPerseus: Randomized point-based value iteration for POMDPs
Spaan, M. T. J.; Vlassis, Nikos UL

in Journal of Artificial Intelligence Research (2005), 24

Partially observable Markov decision processes (POMDPs) form an attractive and principled framework for agent planning under uncertainty. Point-based approximate techniques for POMDPs compute a policy ... [more ▼]

Partially observable Markov decision processes (POMDPs) form an attractive and principled framework for agent planning under uncertainty. Point-based approximate techniques for POMDPs compute a policy based on a finite set of points collected in advance from the agent's belief space. We present a randomized point-based value iteration algorithm called Perseus. The algorithm performs approximate value backup stages, ensuring that in each backup stage the value of each point in the belief set is improved; the key observation is that a single backup may improve the value of many belief points. Contrary to other point-based methods, Perseus backs up only a (randomly selected) subset of points in the belief set, sufficient for improving the value of each belief point in the set. We show how the same idea can be extended to dealing with continuous action spaces. Experimental results show the potential of Perseus in large scale POMDP problems. [less ▲]

Detailed reference viewed: 67 (0 UL)
Peer Reviewed
See detailPlanning with Continuous Actions in Partially Observable Environments
Spaan, M. T. J.; Vlassis, Nikos UL

in Proc. IEEE Int. Conf. on Robotics and Automation (2005)

We present a simple randomized POMDP al gorithm for planning with continuous actions in partially observable environments. Our algorithm operates on a set of reachable belief points, sampled by letting ... [more ▼]

We present a simple randomized POMDP al gorithm for planning with continuous actions in partially observable environments. Our algorithm operates on a set of reachable belief points, sampled by letting the robot interact randomly with the environment. We perform value iteration steps, ensuring that in each step the value of all sampled belief points is improved. The idea here is that by sampling actions from a continuous action space we can quickly improve the value of all belief points in the set. We demonstrate the viability of our algorithm on two sets of experiments: one involving an active localization task and one concerning robot navigation in a perceptually aliased of fice environment. [less ▲]

Detailed reference viewed: 54 (0 UL)