Michael Anderson

In: B. Hardy-Vallee & N. Payette, eds. Beyond the brain: embodied, situated & distributed cognition. (Cambridge: Cambridge Scholar’s Press), in press. Abstract: In this article, I do three main things: 1. First, I introduce an approach to the mind motivated primarily by evolutionary considerations. I do that by laying out four principles for the study of the mind from an evolutionary perspective, and four predictions that they suggest. This evolutionary perspective is completely compatible with, although broader than, the embodied cognition approach. 2. Then I look at one prediction in depth, the idea that the brain evolved by exaptation–reusing exiting functional units, and combining them in novel ways to generate new cognitive capacities. 3. Finally, I try to lay out some of the implications, both of the in-depth example, and of the more general approach.

Artificial Intelligence, in press. Abstract: For some time we have been developing, and have had significant practical success with, a time-sensitive, contradiction-tolerant logical reasoning engine called the active logic machine (ALMA). The current paper details a semantics for a general version of the underlying logical formalism, active logic. Central to active logic are special rules controlling the inheritance of beliefs in general (and of beliefs about the current time in particular), very tight controls on what can be derived from direct contradictions (P &￢P), and mechanisms allowing an agent to represent and reason about its own beliefs and past reasoning. Furthermore, inspired by the notion that until an agent notices that a set of beliefs is contradictory, that set seems consistent (and the agent therefore reasons with it as if it were consistent), we introduce an “apperception function” that represents an agent’s limited awareness of its own beliefs, and serves to modify inconsistent belief sets so as to yield consistent sets. Using these ideas, we introduce a new definition of logical consequence in the context of active logic, as well as a new definition of soundness such that, when reasoning with consistent premises, all classically sound rules remain sound in our new sense. However, not everything that is classically sound remains sound in our sense, for by classical definitions, all rules with contradictory premises are vacuously sound, whereas in active logic not everything follows from a contradiction.

When things go badly, we notice that something is amiss, figure out what went wrong and why, and attempt to repair the problem. Artificial systems depend on their human designers to program in responses to every eventuality and therefore typically don’t even notice when things go wrong, following their programming over the proverbial, and in some cases literal, cliff. This article describes our work on the Meta-Cognitive Loop, a domain-general approach to giving artificial systems the ability to notice, assess, and repair problems. The goal is to make artificial systems more robust.