Drew McDermott

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.

Logic is useful as a neutral formalism for expressing the contents of mental representations. It can be used to extract crisp conclusions regarding the higher-order theory of phenomenal consciousness developed in (McDermott 2001, 20007). A key aspect of conscious perceptions is their connection to the distinction between appearance and reality. Perceptions must often be corrected. To do so requires that the logic of perception be able to represent the logical structure of judgment events, that is, to include the formulas of the logic as objects to be reasoned about. However, there is a limit to how finely humans can examine their own representations. Terms representing primary and secondary qualities seemed to be _locked,_ so that the numbers (or levels of neural activation) that are their essence are not directly accessible. Humans feel a need to invoke ``intrinsic,'' ``nonrelational'' properties of many secondary qualities --- their _qualia_ --- to ``explicate'' how we compare and discriminate among them, although this is not actually how the comparisons are accomplished. This model of qualia explains several things: It accounts for the difference between ``normal'' and ``introspective'' access to a perceptual module in terms of quotation. It dissolves Jackson's knowledge argument by explaining what Mary learns as a fictional but undoubtable belief structure. It makes spectrum inversion logically impossible by providing a degree of freedom between the physical structure of the brain and the representations it contains that redescribes putative cases of spectrum inversion as alternative but equivalent ways of mapping physical states to representational states.