Adversarial decision and optimization-based models
Metadata
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Universidad de Granada
Departamento
Universidad de Granada. Departamento de Ciencias de la Computación e Inteligencia ArtificialMateria
Lógica difusa Incertidumbre (Teoría de la información) Optimización matemática Algoritmos para ordenador Sistemas de soporte a la decisión Modelos
Materia UDC
510 519.25 1100
Date
2016Fecha lectura
2015-11-27Referencia bibliográfica
Villacorta Iglesias, P.J. Adversarial decision and optimization-based models. Granada: Universidad de Granada, 2016. [http://hdl.handle.net/10481/41297]
Sponsorship
Tesis Univ. Granada. Departamento de Ciencias de la Computación e Inteligencia Artificial; Esta tesis ha sido financiada parcialmente por los proyectos P07-TIC-02970 y P11-TIC-8001 de la Junta de Andalucía, TIN2011-27696-C02-01 del Ministerio de Economía y Competitividad, GENIL-PYR-2014-9 del CEI-BioTIC (Universidad de Granada), TIN2008-01948 y TIN2008-06872-C04-04 del Ministerio de Ciencia e Innovación, y por la beca FPU referencia AP-2010-4738 del Ministerio de Educación.Abstract
Decision making is all around us. Everyone makes choices everyday, from
the moment we open our eyes in the morning. Some of them do not have
very important consequences in our life and these consequences are easy to
take into account. However, in the business world, managers make decisions
that have important consequences on the future of their own firm (in terms
of revenues, market position, business policy) and their employees. In these
cases, it is difficult to account for all the possible alternatives and consequences
and to quantify them. Decision making tools such as Decision Analysis are
required in order to determine the optimal decision.
Furthermore, when several competing agents are involved in a decision
making situation and their combination of actions affect each other’s revenues,
the problem becomes even more complicated. The way an agent makes a
decision and the tools required to determine the optimal decision change.
When we are aware of someone observing and reacting to our behavior, one
might occasionally prefer a sub-optimal choice aimed at causing confusion on
the adversary, so that it will be more difficult for him to guess our decision
in future encounters, which may report us a larger benefit. This situation
arises in counter-terrorist combat, terrorism prevention, military domains,
homeland security, computer games, intelligent training systems, economic
adversarial domains, and more.
In simple terms, we define an adversary as an entity whose aims are
somehow inversely related to ours, and who may influence the profits we
obtain from our decisions by taking his/her own actions. This kind of
competitive interaction between two agents fits a variety of complex situations
which can be analyzed with a number of techniques ranging from Knowledge
Engineering and Artificial Intelligence (agent-based modeling, tree exploration, machine learning) to Operational Research, with an emphasis on Game
Theory.
The objective of this thesis is the analysis and design of adversarial
decision and optimization-based models which are able to represent adversarial
situations. We are going to conduct theoretical studies and propose practical
applications including imitation games, security games and patrolling domains.
More precisely, we first study a two-agent imitation game in which one of
the agents does not know the motivation of the other, and tries to predict
his decisions when repeatedly engaging in a conflict situation, by observing
and annotating the past decisions. We propose randomized strategies for
the agents and study their performance from a theoretical and empirical
point of view. Several variants of this situation are analyzed. Then we move
on to practical applications. We address the problem of extracting more
useful information from observations of a randomized Markovian strategy
that arises when solving a patrolling model, and propose a mathematical
procedure based on fuzzy sets and fuzzy numbers for which we provide
a ready-to-use implementation in an R package. Finally, we develop an
application of adversarial reasoning to the problem of patrolling an area
using an autonomous aerial vehicle to protect it against terrestrial intruders,
and solve it using a mix of game-theoretic techniques and metaheuristics.