Formalizing Deceptive Reasoning in Breaking Bad : Default Reasoning in a Doxastic Logic

摘要

The rich expressivity provided by the cognitive event calculus (CEC) knowledge representation framework allows for reasoning over deeply nested beliefs, desires, intentions, and so on. I put CEC to the test by attempting to model the complex reasoning and deceptive planning used in an episode of the popular television show Breaking Bad. CEC is used to represent the knowledge used by reasoners coming up with plans like the ones devised by the fictional characters I describe. However, it becomes clear that a form of nonmonotonic reasoning is necessary—specifically so that an agent can reason about the nonmonotonic beliefs of another agent. I show how CEC can be augmented to have this ability, and then provide examples detailing how my proposed augmentation enables much of the reasoning used by agents such as the Breaking Bad characters. I close by discussing what sort of reasoning tool would be necessary to implement such nonmonotonic reasoning. An old joke, said to be a favorite of Sigmund Freud, opens with two passengers, Trofim and Pavel, on a train leaving Moscow. Trofim begins by confronting Pavel, demanding to know where he is going. Pavel: “To Pinsk.” Trofim: “Liar! You say you are going to Pinsk in order to make me believe you are going to Minsk. But I know you are going to Pinsk!” (Cohen 2002) Fictional stories can sometimes capture aspects of deception in the real world, especially between individuals who are skilled at reasoning over the beliefs of others (secondorder beliefs), the beliefs of one party about the beliefs of another (third-order beliefs), and so on. For example, an agent a desiring to deceive agent b may need to take into account agent b’s counter-deception measures (where the latter measures may be directed back at agent a, as was suspected by poor Trofim). Such fictional stories may thus sometimes be a suitable source of test cases for frameworks specializing in the representation of, and reasoning over, complex doxastic statements. The cognitive event calculus (CEC) promises to be such a framework, given its ability to represent beliefs, knowledge, intentions, and desires over time (Arkoudas and Bringsjord 2009). Copyright c © 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. In this paper, I will attempt to model the reasoning used by agents in an episode of the television series Breaking Bad. Episode 13 of season 5, entitled To’hajiilee, is notably rich in deceptive behaviors between characters, being a point in the series’ overall story arc where the conflict between several consistently wily characters comes to a climax. One group (Jesse and Hank) devises a plan to lure, trap, and catch another character (Walt), and I try to answer two questions about their plan in this paper: First, what sort of reasoning and knowledge representation would be necessary to devise such a plan as the one created by Jesse and Hank? Second, is CEC sufficiently powerful to represent such knowledge and serve as a base framework for such reasoning? Section 1 will argue that even an analysis of how well CEC can model reasoning in a fictional story can be beneficial to the field of automated human-level reasoning, discussing related literature. I give an overview of CEC in Section 2, followed by a synopsis of the relevant portions of To’hajiilee’s plot (Section 3.1). An analysis of the plan generation used by the characters1 in Section 3.2 suggests the need for a form of nonmonotonic reasoning that requires, at a minimum, reasoning over second-order beliefs. I then spend some time explaining how this nonmonotonic reasoning can work in CEC. The paper wraps up with a discussion of implications for the future of deceptive and counter-deceptive AI (Section 5). 1 Why Bother Modeling Reasoning in Plots? The cognition of deception is particularly interesting to model: Knowing when to deceive in social situations may make for robots that are better accepted socially (Wagner and