#### Filter Results:

- Full text PDF available (76)

#### Publication Year

1996

2008

- This year (0)
- Last 5 years (0)
- Last 10 years (12)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Data Set Used

#### Key Phrases

Learn More

- Matthijs T. J. Spaan, Nikos A. Vlassis
- J. Artif. Intell. Res.
- 2005

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… (More)

- Aristidis Likas, Nikos A. Vlassis, Jakob J. Verbeek
- Pattern Recognition
- 2003

We present the global k-means algorithm which is an incremental approach to clustering that dynamically adds one cluster center at a time through a deterministic global search procedure consisting of N (with N being the size of the data set) executions of the k-means algorithm from suitable initial positions. We also propose modifications of the method to… (More)

- Pascal Poupart, Nikos A. Vlassis, Jesse Hoey, Kevin Regan
- ICML
- 2006

Reinforcement learning (RL) was originally proposed as a framework to allow agents to learn in an online fashion as they interact with their environment. Existing RL algorithms come short of achieving this goal because the amount of exploration required is often too costly and/or too time consuming for online learning. As a result, RL is mostly used for… (More)

- Jelle R. Kok, Nikos A. Vlassis
- Journal of Machine Learning Research
- 2006

In this article we describe a set of scalable techniques for learning the behavior of a group of agents in a collaborative multiagent setting. As a basis we use the framework of coordination graphs of Guestrin, Koller, and Parr (2002a) which exploits the dependencies between agents to decompose the global payoff function into a sum of local terms. First, we… (More)

- Nikos A. Vlassis, Aristidis Likas
- Neural Processing Letters
- 2002

Learning a Gaussian mixture with a local algorithm like EM can be difficult because (i) the true number of mixing components is usually unknown, (ii) there is no generally accepted method for parameter initialization, and (iii) the algorithm can get trapped in one of the many local maxima of the likelihood function. In this paper we propose a greedy… (More)

- Josep M. Porta, Nikos A. Vlassis, Matthijs T. J. Spaan, Pascal Poupart
- Journal of Machine Learning Research
- 2006

We propose a novel approach to optimize Partially Observable Markov Decisions Processes (POMDPs) defined on continuous spaces. To date, most algorithms for model-based POMDPs are restricted to discrete states, actions, and observations, but many real-world problems such as, for instance, robot navigation, are naturally defined on continuous spaces. In this… (More)

- Kenichi Kurihara, Max Welling, Nikos A. Vlassis
- NIPS
- 2006

Dirichlet Process (DP) mixture models are promising candidates for clustering applications where the number of clusters is unknown a priori. Due to computational considerations these models are unfortunately unsuitable for large scale data-mining applications. We propose a class of deterministic accelerated DP mixture models that can routinely handle… (More)

- Matthijs T. J. Spaan, Nikos A. Vlassis
- IEEE International Conference on Robotics and…
- 2004

We present an approximate POMDP solution method for robot planning in partially observable environments. Our algorithm belongs to the family of point-based value iteration solution techniques for POMDP, in which planning is performed only on a sampled set of reachable belief points. We describe a simple, randomized procedure that performs value update steps… (More)

- Frans A. Oliehoek, Matthijs T. J. Spaan, Nikos A. Vlassis
- J. Artif. Intell. Res.
- 2008

Decision-theoretic planning is a popular approach to sequential decision making problems, because it treats uncertainty in sensing and acting in a principled way. In single-agent frameworks like MDPs and POMDPs, planning can be carried out by resorting to Q-value functions: an optimal Q-value function Q is computed in a recursive manner by dynamic… (More)

- Pascal Poupart, Nikos A. Vlassis
- ISAIM
- 2008

Bayesian reinforcement learning in partially observable domains is notoriously difficult, in part due to the unknown form of the beliefs and the optimal value function. We show that beliefs represented by mixtures of products of Dirichlet distributions are closed under belief updates for factored domains. Belief monitoring algorithms that use this mixture… (More)