The claim has often been made that passing the Turing Test would not be sufficient to prove that a computer program was intelligent because a trivial program could do it, namely, the “Humongous-Table (HT) Program”, which simply looks up in a table what to say next. This claim is examined in detail. Three ground rules are argued for: (1) That the HT program must be exhaustive, and not be based on some vaguely imagined set of tricks. (2) That the HT (...) program must not be created by some set of sentient beings enacting responses to all possible inputs. (3) That in the current state of cognitive science it must be an open possibility that a computational model of the human mind will be developed that accounts for at least its nonphenomenological properties. Given ground rule 3, the HT program could simply be an “optimized” version of some computational model of a mind, created via the automatic application of program-transformation rules [thus satisfying ground rule 2]. Therefore, whatever mental states one would be willing to impute to an ordinary computational model of the human psyche one should be willing to grant to the optimized version as well. Hence no one could dismiss out of hand the possibility that the HT program was intelligent. This conclusion is important because the Humongous-Table Program Argument is the only argument ever marshalled against the sufficiency of the Turing Test, if we exclude arguments that cognitive science is simply not possible. (shrink)

Much previous work in artificial intelligence has neglected representing time in all its complexity. In particular, it has neglected continuous change and the indeterminacy of the future. To rectify this, I have developed a first‐order temporal logic, in which it is possible to name and prove things about facts, events, plans, and world histories. In particular, the logic provides analyses of causality, continuous change in quantities, the persistence of facts (the frame problem), and the relationship between tasks and actions. It (...) may be possible to implement a temporal‐inference machine based on this logic, which keeps track of several “maps” of a time line, one per possible history. (shrink)

A new theory of problem solving is presented, which embeds problem solving in the theory of action; in this theory, a problem is just a difficult action. Making this work requires a sophisticated language for‐talking about plans and their execution. This language allows a broad range of types of action, and can also be used to express rules for choosing and scheduling plans. To ensure flexibility, the problem solver consists of an interpreter driven by a theorem prover which actually manipulates (...) formulas of the language. Many examples of the use of the system six given. including an extended treatment of the world of blocks. Limitations and extensions of the system are discussed at length. It is concluded that a rule‐based problem solver is necessary and feasible, but that much more work remains to be done on the underlying theory of planning and acting. (shrink)

O'Brien & Opie's theory fails to address the issue of consciousness and introspection. They take for granted that once something is experienced, it can be commented on. But introspection requires neural structures that, according to their theory, have nothing to do with experience as such. That makes the tight coupling between the two in humans a mystery.

Assuming our understanding of the brain continues to advance, we will at some point have a computational theory of how access consciousness works. Block's supposed additional kind of consciousness will not appear in this theory, and continued belief in it will be difficult to sustain. Appeals to to experience such-and-such will carry little weight when we cannot locate a subject for whom it might be like something.

Stevan Harnad correctly perceives a deep problem in computationalism, the hypothesis that cognition is computation, namely, that the symbols manipulated by a computational entity do not automatically mean anything. Perhaps, he proposes, transducers and neural nets will not have this problem. His analysis goes wrong from the start, because computationalism is not as rigid a set of theories as he thinks. Transducers and neural nets are just two kinds of computational system, among many, and any solution to the semantic problem (...) that works for them will work for most other computational systems. (shrink)

Drew McDermott, Int. J. Mach. Conscious., 06, 45 (2014). DOI: 10.1142/S1793843014400071.

This is a review of Drew V. McDermott, Mind and Mechanism, MIT Press, Cambridge, MA, 2001.

[This is a review of: Drew McDermott, Mind and Mechanism, MIT Press, Cambridge, MA, 2001.] -/- The author is interested in computational approaches to consciousness. His reason for working in the field of AI is to solve the mind-body problem, that is, to understand how the brain can have experiences. This is an intricate project because it involves elucidation of the relationship between our mentality and its physical foundation. How can a biological/chemical system (the human body) have experiences, (...) beliefs, desires, intentions, and so on? Physicists have good reasons to persuade us that ours is a material world that obeys physical laws. Once we commit ourselves to this view, it sounds quite bewildering to think that there is a place for independently existing minds in such a world. When physicists speak, McDermott listens. His hypothesis is that we are all contraptions designed by evolution. The concept of mind arises because people’s brains are biological computers. Crudely put, minds are produced by brains. The essential contribution of this rich book is an extended argument about how one can be more specific about the way this production is realized. (shrink)

Semantic Engines: An Introduction to Mind Design, John C. Haugeland; Computer Science as Empirical Inquiry: Symbols and Search, Alan Newell and Herbert A. Simon; Complexity and the Study of Artificial and Human Intelligence, Zenon Pylyshyn; A Framework for Representing Knowledge, Marvin Minsky; Artificial Intelligence---A Personal View, David Marr; Artificial Intelligence Meets Natural Stupidity, Drew McDermott; From Micro-Worlds to Knowledge Representation: AI at an Impasse, Hubert L. Dreyfus; Reductionism and the Nature of Psychology, Hilary Putnam; Intentional Systems, Daniel C. (...) Dennett; The Nature and Plausibility of Cognitivism, John C. Haugeland; Minds, Brains, and Programs, John R. Searle; MethodologicalSolipsism Considered as a Research Strategy in Cognitive Psychology, Jerry A. Fodor; The Material Mind, Donald Davidson. (shrink)

The Yale Shooting Problem introduced by Steve Hanks & Drew McDermott (1987) is a well-known test case of non-monotonic temporal reasoning. There is a sequence of situations. In the initial situation a gun is not loaded and the target is alive. In the next situation the gun is loaded. Eventually, a shot is fired, perhaps with fatal consequences. In this scenario there are two "fluents", alive and loaded, and two actions, load and shoot. Being loaded and being alive (...) are inert propositions in the sense that if they are true at a given moment, they will be true at the next moment unless some action such as.. (shrink)

The Little Nell Problem was formulated by Drew McDermott in 1982. It reveals unexpected complexities in the interaction of the beliefs and intentions of a planning agent. This paper discusses the problem and proposes a solution.

Qualitative reasoning about space and time - a reasoning at the human level - promises to become a fundamental aspect of future systems that will accompany us in daily activity. The aim of Spatial and Temporal Reasoning is to give a picture of current research in this area focusing on both representational and computational issues. The picture emphasizes some major lines of development in this multifaceted, constantly growing area. The material in the book also shows some common ground and a (...) novel combination of spatial and temporal aspects of qualitative reasoning. Part I presents the overall scene. The chapter by Laure Vieu is on the state of the art in spatial representation and reasoning, and that by Alfonso Gerevini gives a similar survey on research in temporal reasoning. The specific contributions to these areas are then grouped in the two main parts. In Part II, Roberto Casati and Achille Varzi examine the ontological status of spatial entities; Anthony Cohn, Brandon Bennett, John Gooday, and Nicholas Gotts present a detailed theory of reasoning with qualitative relations about regions; Andrew Frank discusses the spatial needs of geographical information systems; and Annette Herskovits focuses on the linguistic expression of spatial relations. In Part III, James Allen and George Ferguson describe an interval temporal logic for the representation of actions and events; Drew McDermott presents an efficient way of predicting the outcome of plan execution; and Erik Sandewall introduces a semantics based on transitions for assessing theories of action and change. In Part IV, Antony Galton's chapter stands clearly between the two areas of space and time and outlines the main coordinates of an integrated approach. (shrink)

The lived body has structures of ability built up over time through habit. Serious illness, injury, and incapacity can disrupt these capacities, and thereby, one’s relationship to the body, and to time itself. This paper focuses attention on a series of healing strategies individuals then employ on the “chessboard” of possibilities intrinsic to lived embodiment. This can include restoring past abilities (pointing to the future to recreate the past); and/or transforming one’s bodily structure or use-patterns, or the external environment, to (...) compensate. With many conditions, including progressive aging, no full rehabilitation is possible. Nevertheless, one can also seek consolation, richness, or hope by remembering the past; anticipating the future; or presencing, that is, living fully in the now. Insofar as past, present, and future are interwoven in one’s life experience, many also meet adversity by a life-story revision, constructing a new narrative to render events meaningful. Some also access a sense of transpersonal timelessness, whether through anticipation of an afterlife, or a sense of the eternal present. There is a literature on the adaptive coping mechanism used by the chronically ill, but with its diverse patient populations, methodologies, and categories, it has proved difficult to systematize. This article suggests that the structures of lived embodiment, as explored by phenomenology, provide a way to understand the modes of wholeness individuals access over time, and in relation to time—what is here termed chronic healing. (shrink)

In Feel-Bad Postfeminism, Catherine McDermott provides crucial insight into what growing up during empowerment postfeminism feels like, and outlines the continuing postfeminist legacy of resilience in girlhood coming-of-age narratives. McDermott's analysis of Gone Girl (2012), Girls (2012-2017) and Appropriate Behaviour (2012) illuminates a major cultural turn in which the pleasures of postfeminist empowerment curdle into a profound sense of rage and resentment. By contrast, close examination of The Hunger Games (2008-2010), Girlhood (2014) and Catch Me Daddy (2014) reveals (...) that contemporary genres are increasingly constructing girls as uniquely capable of resiliently overcoming and adapting to unforgiving social conditions. She develops an affective vocabulary to better understand contemporary modes of defiant, transformative and relational resilience, as well as a framework through which to expand on further modes that are specific to the genres they emerge within. Overall, the book suggests that exploration of the affective dimensions of girls' and women's culture can offer new insights into how coming-of-age, girlhood and femininity are culturally produced in the aftermath of postfeminism. (shrink)