Ramón López de Mántaras

EUMAS Conference 2016 Conference Paper

The Argumentative Mediator

• Carles Sierra
• Ramón López de Mántaras
• Simeon J. Simoff

Abstract In this paper we introduce a negotiation mediator in a multiagent context. When negotiation fails, a mediator can interact with the parties, find out about their goals, ontologies, and arguments for and against negotiation outcome, and suggest solutions based on previous experience. An algorithmic schema to be instantiated with particular argumentation, semantic alignment and case-base reasoning techniques is presented. The proposal is neutral with respect to which particular technique is selected. An example illustrates the approach that is framed in the existing body of literature on argumentation and mediation.

ECAI Conference 2010 Conference Paper

Case-Based Multiagent Reinforcement Learning: Cases as Heuristics for Selection of Actions

• Reinaldo A. C. Bianchi
• Ramón López de Mántaras

This work presents a new approach that allows the use of cases in a case base as heuristics to speed up Multiagent Reinforcement Learning algorithms, combining Case-Based Reasoning (CBR) and Multiagent Reinforcement Learning (MRL) techniques. This approach, called Case-Based Heuristically Accelerated Multiagent Reinforcement Learning (CB-HAMRL), builds upon an emerging technique, Heuristic Accelerated Reinforcement Learning (HARL), in which RL methods are accelerated by making use of heuristic information. CB-HAMRL is a subset of MRL that makes use of a heuristic function [Hscr ] derived from a case base, in a Case-Based Reasoning manner. An algorithm that incorporates CBR techniques into the Heuristically Accelerated Minimax–Q is also proposed and a set of empirical evaluations were conducted in a simulator for the Littman's robot soccer domain, comparing the three solutions for this problem: MRL, HAMRL and CB-HAMRL. Experimental results show that using CB-HAMRL, the agents learn faster than using RL or HAMRL methods.

ECAI Conference 2008 Conference Paper

Learning to Select Object Recognition Methods for Autonomous Mobile Robots

• Reinaldo A. C. Bianchi
• Arnau Ramisa
• Ramón López de Mántaras

Selecting which algorithms should be used by a mobile robot computer vision system is a decision that is usually made a priori by the system developer, based on past experience and intuition, not systematically taking into account information that can be found in the images and in the visual process itself to learn which algorithm should be used, in execution time. This paper presents a method that uses Reinforcement Learning to decide which algorithm should be used to recognize objects seen by a mobile robot in an indoor environment, based on simple attributes extracted on-line from the images, such as mean intensity and intensity deviation. Two state-of-the-art object recognition algorithms can be selected: the constellation method proposed by Lowe together with its interest point detector and descriptor, the Scale-Invariant Feature Transform and a bag of features approach. A set of empirical evaluations was conducted using a household mobile robots image database, and results obtained shows that the approach adopted here is very promising.

ICRA Conference 2008 Conference Paper

Mobile robot localization using panoramic vision and combinations of feature region detectors

• Arnau Ramisa
• Adriana Tapus
• Ramón López de Mántaras
• Ricardo Toledo

This paper presents a vision-based approach for mobile robot localization. The environmental model is topological. The new approach uses a constellation of different types of affine covariant regions to characterize a place. This type of representation permits a reliable and distinctive environment modeling. The performance of the proposed approach is evaluated using a database of panoramic images from different rooms. Additionally, we compare different combinations of complementary feature region detectors to find the one that achieves the best results. Our experimental results show promising results for this new localization method. Additionally, similarly to what happens with single detectors, different combinations exhibit different strengths and weaknesses depending on the situation, suggesting that a context-aware method to combine the different detectors would improve the localization results.

ICML Conference 2003 Conference Paper

Tractable Bayesian Learning of Tree Augmented Naive Bayes Models

• Jesús Cerquides
• Ramón López de Mántaras

ICML Conference 2002 Conference Paper

Action Refinement in Reinforcement Learning by Probability Smoothing

• Thomas G. Dietterich
• Dídac Busquets
• Ramón López de Mántaras
• Carles Sierra

ECAI Conference 1998 Invited Paper

It Don't Mean A Thing (If It Ain't Got That Swing)

• Ramón López de Mántaras

ECAI Conference 1998 Conference Paper

Possibility Theory-Based Environment Modelling by Means of Behaviour-Based Autonomous Robots

• Maite López-Sánchez
• Ramón López de Mántaras
• Carles Sierra

UAI Conference 1997 Conference Paper

Incremental Map Generation by Low Cost Robots Based on Possibility/Necessity Grids

• Maite López-Sánchez
• Ramón López de Mántaras
• Carles Sierra

UAI Conference 1992 Conference Paper

A Symbolic Approach to Reasoning with Linguistic Quantifiers

• Didier Dubois
• Henri Prade
• Lluís Godo
• Ramón López de Mántaras

ECAI Conference 1988 Conference Paper

Model-Based Knowledge Acquisition for Heuristic Classification Systems

• Enric Plaza
• Ramón López de Mántaras