The notion of Simulative Reasoning in the study of propositional attitudes within Artificial Intelligence (AI) is strongly related to the Simulation Theory of mental ascription in Philosophy. Roughly speaking, when an AI system engages in Simulative Reasoning about a target agent, it reasons with that agent’s beliefs as temporary hypotheses of its own, thereby coming to conclusions about what the agent might conclude or might have concluded. The contrast is with non-simulative meta-reasoning, where the AI system reasons within a detailed (...) theory about the agent’s (conjectured) reasoning acts. The motive within AI for preferring Simulative Reasoning is that it is more convenient and efficient, because of a simplification of the representations and reasoning processes. The chapter discusses this advantage in detail. It also sketches the use of Simulative Reasoning in an AI natural language processing system, ATT-Meta, that is currently being implemented. This system is directed at the understanding of propositional attitude reports. In ATT-Meta, Simulative Reasoning is yoked to a somewhat independent set of ideas about how attitude reports should be treated. Central here are the claims that (a) speakers often employ commonsense (and largely metaphorical) models of mind in describing agents’ attitudes, (b) the listener accordingly needs often to reason within the terms of such models, rather than on the basis of any objectively justifiable characterization of the mind, and (c) the commonsense models filter the suggestions that Simulative Reasoning comes up with concerning target agents’ reasoning conclusions. There is a yet tighter connection between the commonsense models and the Simulative Reasoning. It turns out that Simulative Reasoning can be rationally reconstructed in terms of a more general type of reasoning about the possibly-counterfactual “world” that the target agent believes in, together with an assumption that that agent has a faithful representation of the world. In the ATT-Meta approach, the reasoner adopts that assumption when it views the target agent through a particular commonsense model (called IDEAS-AS-MODELS). (shrink)

Artificial Intelligence: A Modern Approach, 3e offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. Dr. Peter Norvig, contributing Artificial Intelligence author and Professor Sebastian Thrun, a Pearson author are offering a free online course at Stanford University on artificial intelligence. According to an article in The New York Times, the course on artificial intelligence is (...) “one of three being offered experimentally by the Stanford computer science department to extend technology knowledge and skills beyond this elite campus to the entire world.” One of the other two courses, an introduction to database software, is being taught by Pearson author Dr. Jennifer Widom. Artificial Intelligence: A Modern Approach, 3e is available to purchase as an eText for your Kindle™, NOOK™, and the iPhone®/iPad®. (shrink)

I sketch my general model of the roles of intentions in the planning of agents like us-agents with substantial resource limitations and with important needs for coordination. I then focus on the stability of prior intentions: their rational resistance to reconsideration. I emphasize the importance of cases in which one's nonreconsideration of a prior intention is nondeliberative and is grounded in relevant habits of reconsideration. Concerning such cases I argue for a limited form of two-tier consequentialism, one that is restricted (...) in ways that aim at blocking an analogue of Smart's concerns about rule-worship. I contrast this with the unrestricted two-tier consequentialism suggested by McClennen. I argue that my restricted approach is superior for a theory of the practical rationality of reflective, planning agents like us. But I also conjecture that an unrestricted two-tier consequentialism may be more appropriate for the AI project of specifying a high level architecture for a resource-bounded planner. (shrink)

The tension between rule and judgment is well known with respect to the meaning of substantive legal commands. The same conflict is present in fact finding. The law penetrates to virtually all aspects of human affairs; irtually any interaction can generate a legal conflict. Accurate fact finding about such disputes is a necessary condition for the appropriate application of substantive legal commands. Without accuracy in fact finding, the law is unpredictable, and thus individuals cannot efficiently accommodate their affairs to its (...) commands. The need for accuracy and predictability in legal fact finding has generated a search for formal tools to apply to the task. Among the tools that have been examined are Bayes' Theorem and expected utility theory (Bayesian or statistical decision theory). These tools do not map well onto trials, which in turn has generated an examination of alternative approaches, in particular the story model and the relative plausibility theory. This paper discusses these issues in turn. It elaborates the basic structure of trials in the American tradition; examines the uneasy relationship between trials and such formalisms as Bayes' Theorem and expected utility theory; and introduces the relative plausibility theory as an explanation of the nature of juridical proof. (shrink)

Stephen Stich and Shaun Nichols advance the debate over folk psychology with their vivid depiction of the contest between the simulation theory and the theory-theory (Stich & Nichols, this issue). At least two aspects of their presentation I find highly congenial. First, they give a generally fair characterization of the simulation theory, in some respects even improving its formulation. Though I have a few minor quarrels with their formulation, it is mostly quite faithful to the version which I have found (...) attractive (Goldman, 1989).Second, I concur with Stich and Nichols in their assertion that the theory-theory and the simulation theory are the only two games in town. Where I disagree, of course, is in Stich and Nichols’s contention that their arguments so dim the prospects of the simulation theory that it should no longer be taken seriously. On the contrary, I shall maintain, the simulation theory remains a very plausible contender, and should receive careful and respectful consideration in the continuing investigation. (shrink)