CAOS Coach 2006 Simulation Team: An Opponent Modelling Approach

Agent technology represents a very interesting new means for analyzing, designing and building complex software systems. Nowadays, agent modelling in multi-agent systems is increasingly becoming more complex and significant. RoboCup Coach Competition is an exciting competition in the RoboCup Soccer League and its main goal is to encourage research in multii-agent modelling. This paper describes a novel method used by the team CAOS (CAOS Coach 2006 Simulation Team) in this competition. The objective of the team is to model successfully the behaviour of a multi-agent system

CAOS Coach 2006 Simulation Team: an opponent modelling approach

Agent technology l'epresents a vel'Y intel'esting new means for analyzing, designing and building complex software systems. Nowadays, agent modelling in multi-agent systems is increasingly becoming more complex and significant. RoboCup Coach Cornpetition is an exciting competition in the RoboCup Soccel' League and its main goal is to encourage reseal'ch in multiagent modelling. This papel' describes a novel method used by the team CAOS (CAOS Coach 2006 Simulation Tearn) in this competition. The objective of the team is to model successfully the behaviour of a multi-agent system.This work has been supported by the Spanish Ministry of Education and Science under project TRA-2007-67374-C02-02

Deep learning based approaches for imitation learning.

Imitation learning refers to an agent's ability to mimic a desired behaviour by learning from observations. The field is rapidly gaining attention due to recent advances in computational and communication capabilities as well as rising demand for intelligent applications. The goal of imitation learning is to describe the desired behaviour by providing demonstrations rather than instructions. This enables agents to learn complex behaviours with general learning methods that require minimal task specific information. However, imitation learning faces many challenges. The objective of this thesis is to advance the state of the art in imitation learning by adopting deep learning methods to address two major challenges of learning from demonstrations. Firstly, representing the demonstrations in a manner that is adequate for learning. We propose novel Convolutional Neural Networks (CNN) based methods to automatically extract feature representations from raw visual demonstrations and learn to replicate the demonstrated behaviour. This alleviates the need for task specific feature extraction and provides a general learning process that is adequate for multiple problems. The second challenge is generalizing a policy over unseen situations in the training demonstrations. This is a common problem because demonstrations typically show the best way to perform a task and don't offer any information about recovering from suboptimal actions. Several methods are investigated to improve the agent's generalization ability based on its initial performance. Our contributions in this area are three fold. Firstly, we propose an active data aggregation method that queries the demonstrator in situations of low confidence. Secondly, we investigate combining learning from demonstrations and reinforcement learning. A deep reward shaping method is proposed that learns a potential reward function from demonstrations. Finally, memory architectures in deep neural networks are investigated to provide context to the agent when taking actions. Using recurrent neural networks addresses the dependency between the state-action sequences taken by the agent. The experiments are conducted in simulated environments on 2D and 3D navigation tasks that are learned from raw visual data, as well as a 2D soccer simulator. The proposed methods are compared to state of the art deep reinforcement learning methods. The results show that deep learning architectures can learn suitable representations from raw visual data and effectively map them to atomic actions. The proposed methods for addressing generalization show improvements over using supervised learning and reinforcement learning alone. The results are thoroughly analysed to identify the benefits of each approach and situations in which it is most suitable

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Recognizing Teamwork Activity In Observations Of Embodied Agents

This thesis presents contributions to the theory and practice of team activity recognition. A particular focus of our work was to improve our ability to collect and label representative samples, thus making the team activity recognition more efficient. A second focus of our work is improving the robustness of the recognition process in the presence of noisy and distorted data. The main contributions of this thesis are as follows: We developed a software tool, the Teamwork Scenario Editor (TSE), for the acquisition, segmentation and labeling of teamwork data. Using the TSE we acquired a corpus of labeled team actions both from synthetic and real world sources. We developed an approach through which representations of idealized team actions can be acquired in form of Hidden Markov Models which are trained using a small set of representative examples segmented and labeled with the TSE. We developed set of team-oriented feature functions, which extract discrete features from the high-dimensional continuous data. The features were chosen such that they mimic the features used by humans when recognizing teamwork actions. We developed a technique to recognize the likely roles played by agents in teams even before the team action was recognized. Through experimental studies we show that the feature functions and role recognition module significantly increase the recognition accuracy, while allowing arbitrary shuffled inputs and noisy data