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)

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.

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.

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.