Desarrollo de un sistema de juego al Tres en Raya para el robot NAO H25

En este trabajo se presenta el desarrollo de un módulo para el robot NAO que le permitirá jugar al “Tres en Raya” con un humano de forma autónoma. El desarrollo del juego incluye una gran variedad de técnicas de inteligencia artificial como el aprendizaje por demostración, la visión artificial y la búsqueda heurística que le permitirán alcanzar al robot ese grado de autonomía. La finalidad del trabajo es conseguir que el robot NAO aumente el componente social e interactúe cada vez mejor con los humanos mediante juegos sencillos que todos conocemos, pues este trabajo sirve de base para desarrollar otros módulos con otros juegos diferentes. En la realización del proyecto se ha utilizado el simulador Choregraphe y el API de NaoQi, desarrollados por Aldebaran Robotics para trabajar expresamente con el robot NAO y las librerías de programación OpenCV, para el procesamiento de imágenes, y AIMA, para el algoritmo del juego. El código se ha desarrollado en Python. El proyecto se ha probado sobre un entorno real, con un jugador humano y un robot NAO.This thesis presents the design and development of a new module for NAO, the humanoid robot, which allows it to play Tic Tac Toe against a human player in an autonomous way. The development includes a wide variety of artificial intelligence based techniques such as Learning From Demonstration, Computer Vision and Heuristic Search in order to reach that autonomous behavior on the robot. The aim of this work is to get the robot NAO increase its social component to improve the interaction with humans through simple well-known games. This work serves as the basis to develop other modules with different games. The code of the Tic Tac Toe module has been developed in Python with the help of Aldebaran’s Choregraphe simulator and NaoQi API to work with the robot, OpenCV for image processing and AIMA library for the algorithm of the game. The module has been tested in a real environment where a Tic Tac Toe game was played by a human and a robot NAO.Ingeniería Informátic

Plan merging by reuse for multi-agent planning

Multi-Agent Planning deals with the task of generating a plan for/by a set of agents that jointly solve a planning problem. One of the biggest challenges is how to handle interactions arising from agents' actions. The first contribution of the paper is Plan Merging by Reuse, pmr, an algorithm that automatically adjusts its behaviour to the level of interaction. Given a multi-agent planning task, pmr assigns goals to specific agents. The chosen agents solve their individual planning tasks and the resulting plans are merged. Since merged plans are not always valid, pmr performs planning by reuse to generate a valid plan. The second contribution of the paper is rrpt-plan, a stochastic plan-reuse planner that combines plan reuse, standard search and sampling. We have performed extensive sets of experiments in order to analyze the performance of pmr in relation to state of the art multi-agent planning techniques.This work has been partially supported by the MINECO projects TIN2017-88476-C2-2-R, RTC-2016-5407-4, and TIN2014-55637-C2-1-R and MICINN project TIN2011-27652-C03-02

OpinAIS: An Artificial Immune System-based Framework for Opinion Mining

This paper proposes the design of an evolutionary algorithm for building classifiers specifically aimed towards performing classification and sentiment analysis over texts. Moreover, it has properties taken from Artificial Immune Systems, as it tries to resemble biological systems since they are able to discriminate harmful from innocuous bodies (in this case, the analogy could be established with negative and positive texts respectively). A framework, namely OpinAIS, is developed around the evolutionary algorithm, which makes it possible to distribute it as an open-source tool, which enables the scientific community both to extend it and improve it. The framework is evaluated with two different public datasets, the first involving voting records for the US Congress and the second consisting in a Twitter corpus with tweets about different technology brands, which can be polarized either towards positive or negative feelings; comparing the results with alternative machine learning techniques and concluding with encouraging results. Additionally, as the framework is publicly available for download, researchers can replicate the experiments from this paper or propose new ones

Using Pre-Computed Knowledge for Goal Allocation in Multi-Agent Planning

Many real-world robotic scenarios require performing task planning to decide courses of actions to be executed by (possibly heterogeneous) robots. A classical centralized planning approach has to find a solution inside a search space that contains every possible combination of robots and goals. This leads to inefficient solutions that do not scale well. Multi-Agent Planning (MAP) provides a new way to solve this kind of tasks efficiently. Previous works on MAP have proposed to factorize the problem to decrease the planning effort i.e. dividing the goals among the agents (robots). However, these techniques do not scale when the number of agents and goals grow. Also, in most real world scenarios with big maps, goals might not be reached by every robot so it has a computational cost associated. In this paper we propose a combination of robotics and planning techniques to alleviate and boost the computation of the goal assignment process. We use Actuation Maps (AMs). Given a map, AMs can determine the regions each agent can actuate on. Thus, specific information can be extracted to know which goals can be tackled by each agent, as well as cheaply estimating the cost of using each agent to achieve every goal. Experiments show that when information extracted from AMs is provided to a multi-agent planning algorithm, the goal assignment is significantly faster, speeding-up the planning process considerably. Experiments also show that this approach greatly outperforms classical centralized planning.This work has been partially funded by FEDER/ Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación/TIN2017-88476-C2-2-R and MINECO/TIN2014-55637-C2-1-R. I has been also financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme within project >, and by National Funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UID/EEA/50014/2013, and FCT grant SFRH/BD/52158/2013 through Carnegie Mellon Portugal Program

Plan merging via plan reuse

Mención Internacional en el título de doctorMulti-agent planning deals with the task of generating a plan for/by a set of agents that jointly solve a planning problem. One of the biggest challenges is how to handle interactions arising from agents’ actions. There exist some other relevant challenges such as planner’s scalability when agents, goals or resources increase; or improving the makespan of the resulting plan. In this Thesis, we present Plan Merging by Reuse, PMR, a multi-agent planner that automatically adjusts its behaviour to the level of interaction. Given a multi-agent planning task, PMR decides which agents will try to achieve each goal. The chosen agents solve their individual planning tasks. The resulting plans are merged and PMR checks the plan’s validity. Given the potential interactions, merged plans are not always valid. When that happens, PMR performs planning by reuse to generate a valid plan. In order to deal with the problem of agents’ coordination, another contribution presented on this Thesis is RRPT, a stochastic plan-reuse planner. RRPT is able to adjust itself to two different cases: when the input invalid plan is very similar to the final valid solution (classic plan reuse scenario) and also when the input plan is completely different from the final solution. RRPT combines plan reuse and standard search. It will decide stochastically on each iteration which technique to run. Thus, RRPT adapts itself to a wide variety of scenarios. We have performed extensive sets of experiments in order to analyze when to use the different PMR variants, as well as which tasks are more appropriate to be solved by PMR. Our contributions obtain solutions to multi-agent planning tasks where PMR and RRPT can successfully adapt their behavior to the particularities of the problems.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: Eva Onaindía de la Rivaherrera.- Secretario: Dirk Sascha Ossowski.- Vocal: Michael Wayne Barle

OpinAIS: An Artificial Immune System-based Framework for Opinion Mining

This paper proposes the design of an evolutionary algorithm for building classifiers specifically aimed towards performing classification and sentiment analysis over texts. Moreover, it has properties taken from Artificial Immune Systems, as it tries to resemble biological systems since they are able to discriminate harmful from innocuous bodies (in this case, the analogy could be established with negative and positive texts respectively). A framework, namely OpinAIS, is developed around the evolutionary algorithm, which makes it possible to distribute it as an open-source tool, which enables the scientific community both to extend it and improve it. The framework is evaluated with two different public datasets, the first involving voting records for the US Congress and the second consisting in a Twitter corpus with tweets about different technology brands, which can be polarized either towards positive or negative feelings; comparing the results with alternative machine learning techniques and concluding with encouraging results. Additionally, as the framework is publicly available for download, researchers can replicate the experiments from this paper or propose new ones.This work was partially funded by the Spanish Ministry of Science and Innovation under MOVES project (TIN2011-28336) and European Union's CIP Programme (ICT-PSP-2012) under grant agreement no. 325146 (SEACW project).Publicad