Dissertação
Aprendizado ativo em raciocínio baseado em casos para o emprego do engano em jogos de cartas
Fecha
2020-08-07Autor
Vargas, Daniel Pinheiro
Institución
Resumen
Deception is omnipresent in everyday social interactions. Deception and automatic deception
detection and exploration are research subjects in many different fields, such as cyber security,
computer games, military operations and fake news. Despite these works, the implementation
of intelligent agents capable of deceiving in computer systems remains a challenging task. In
particular, it is not trivial to capture and label the intention of a human strategist when making
a certain deceptive decision, especially if we consider passive learning techniques in Artificial
Intelligence. In this sense, this work proposes a new approach that combines active learning
and case-based reasoning - CBR, in which an agent when faced with situations that require
deceptive decision-making asks a human expert to make a review of the solution suggested by
the CBR algorithms. In this active learning process, if necessary, the expert presents a more
appropriate solution to the current problem. Thereby, this work shows how to systematically
capture experiences of problem solving that involve deception and later use the acquired
knowledge in order to make better decisions when faced with opportune situations for the use
of deception. Experimental results in the domain of a card game called Truco has demonstrated
that the use of active training techniques, compared to imitation learning techniques, enables a
Truco player agent, even using case bases with a reduced number of cases, to play at higher
levels than agents who use much larger case bases