Drew McDermott

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

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.

People and intelligent computers, if there ever are any, will both have to believe certain things in order to be intelligent agents at all, or to be a particular sort of intelligent agent. I distinguish implicit beliefs that are inherent in the architecture of a natural or artificial agent, in the way it is 'wired', from explicit beliefs that are encoded in a way that makes them easier to learn and to erase if proven mistaken. I introduce the term IFI, which stands for irresistible framework intuition, for an implicit belief that can come into conflict with an explicit one. IFIs are a key element of any theory of consciousness that explains qualia and other aspects of phenomenology as second-order beliefs about perception. Before I can survey the IFI landscape, I review evidence that the brains of humans, and presumably of other intelligent agents, consist of many specialized modules that are capable of sharing a unified workspace on urgent occasions, and jointly model themselves as a single agent. I also review previous work relevant to my subject. Then I explore several IFIs, starting with, 'My future actions are free from the control of physical laws'. Most of them are universal, in the sense that they will be shared by any intelligent agent; the case must be argued for each IFI. When made explicit, IFIs may look dubious or counterproductive, but they really are irresistible, so we find ourselves in the odd position of oscillating between justified beliefs and conflicting but irresistible beliefs. We cannot hope that some process of argumentation will resolve the conflict

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

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.