Constrained optimization under uncertainty for decision-making problems: Application to Real-Time Strategy games

Decision-making problems can be modeled as combinatorial optimization problems with Constraint Programming formalisms such as Constrained Optimization Problems. However, few Constraint Programming formalisms can deal with both optimization and uncertainty at the same time, and none of them are convenient to model problems we tackle in this paper. Here, we propose a way to deal with combinatorial optimization problems under uncertainty within the classical Constrained Optimization Problems formalism by injecting the Rank Dependent Utility from decision theory. We also propose a proof of concept of our method to show it is implementable and can solve concrete decision-making problems using a regular constraint solver, and propose a bot that won the partially observable track of the 2018 {\mu}RTS AI competition. Our result shows it is possible to handle uncertainty with regular Constraint Programming solvers, without having to define a new formalism neither to develop dedicated solvers. This brings new perspective to tackle uncertainty in Constraint Programming.Comment: Published at the 2019 IEEE Congress on Evolutionary Computation (CEC'19

Language for Description of Worlds

We will reduce the task of creating AI to the task of finding an appropriate language for description of the world. This will not be a programing language because programing languages describe only computable functions, while our language will describe a somewhat broader class of functions. Another specificity of this language will be that the description will consist of separate modules. This will enable us look for the description of the world automatically such that we discover it module after module. Our approach to the creation of this new language will be to start with a particular world and write the description of that particular world. The point is that the language which can describe this particular world will be appropriate for describing any world

Epistemic Game Master: A referee for GDL-III Games

General Game Playing is the field of Artificial Intelligence that designs agents that are able to understand game rules written in Game Description Language and use them to play those games effectively. A General Game Playing system uses a Game Master, or referee, to control games and players. With the introduction of the latest extension of GDL, the GDL-III enabled to describe epistemic games. However, the complexity of the state space of these new games became in such way large that is impossible for both the players and the manager to reason precisely about GDL-III games. One way to approach this problem is to use an approximative approach, such as model-sampling. This dissertation shows a Game Master that is able to understand and control games in GDL-III and its players, by using model-sampling to sample possible game states. With the development of this Game Master, players can be developed to be able to play GDL-III games without human intervention. Throughout this dissertation, we present details of our developed solution, how we manage to make the Game Master understand a GDL-III game and how we implemented model sampling. Furthermore, we show that our solution, however approximative, has the same capabilities of an non approximative approach while given enough resources. We show how the Game Master timely scales with increasingly bigger epistemic games

Desarrollo de una política de selección para general Game Playing basada en el problema del bandido multi-armado

Durante la historia de la Inteligencia Artificial se han desarrollo diversos agentes inteligentes capaces de jugar juegos de tablero de entre los m´as destacados se tiene a DeepBlue para el ajedrez y AlphaGo para el juego de Go. Sin embargo, estos agentes solo est´an enfocados en un solo juego por lo cual el paso natural es desarrollar agentes capaces de jugar m´as de un juego, idea que persigue el ´area General Game Playing la cual se enfoca en desarrollar agentes completamente aut´onomos que puedan jugar cualquier juego de tablero sin intervenci´on humana y sin conocimiento previo. La mayor´ıa de los agentes est´an basados en el m´etodo de Arbol de B´usqueda Monte Carlo que usa ´ simulaciones Monte Carlo para estimar movimientos prometedores, este m´etodo consiste en cuatro pasos: Selecci´on, Expansi´on, Simulaci´on y Propagaci´on Hacia Atr´as. Los esfuerzos para incrementar el rendimiento del Arbol de B´usqueda Monte Carlo se enfocan en el paso de simulaci´on, sin embargo, ´ es tambi´en el paso de Selecci´on el que repercute en dicho rendimiento. El paso de selecci´on controla la forma en que se recorre el ´arbol asociado al juego de tablero, dicho recorrido es guiado por una Pol´ıtica de Selecci´on de la cual Upper Confidence Bound es la popularmente usada. En esta tesis de investigaci´on se presentan dos pol´ıticas de selecci´on UCBα1 y UCBα2, las cuales est´an basadas en Upper Confidence Bound pero est´an pensadas completamente para su aplicaci´on en General Game Playing. UCBα1 y UCBα2 a diferencia de Upper Confidence Bound aprovechan la estructura del ´arbol asociados a los juegos de tablero para determinar para un nodo padre, cu´anto se debe explorar los nodos hijos que representan los movimientos disponibles antes de decidirse por explotar un nodo hijo con un movimiento prometedor. Upper Confidence Bound controla la exploraci´on de nodos hijos por medio de una constante de exploraci´on, sin embargo, esta constante permanece fija en todo el ´arbol sin importar el n´umero de nodos hijos. UCBα1 y UCBα2 usan una funci´on que depende del

Information Set Search for General Game Playing with Imperfect Information

katedra řídicí technik

HyperPlay : a solution to General Game Playing with imperfect information

General Game Playing is the design of AI systems able to understand the rules of new games and to use such descriptions to play those games effectively. Games with imperfect information have recently been added as a new challenge for existing general game-playing systems. The HyperPlay technique presents a solution to this challenge by maintaining a collection of models of the true game as a foundation for reasoning, and move selection. The technique provides existing game players with a bolt-on solution to convert from perfect-information games to imperfect-information games. In this paper we describe the HyperPlay technique, show how it was adapted for use with a Monte Carlo decision making process and give experimental results for its performance

Stochastic Constraint Programming for General Game Playing with Imperfect Information

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