2018 AAAI Fall Symposium on ‘A Common Model of the Cognition’

A mind is a functional entity that can think, and thus support intelligent behavior. Artificial intelligence, cognitive science, neuroscience, and robotics all contribute to our understanding of minds, although each draws from a different perspective. Artificial intelligence concerns building artificial minds, and thus cares most about how systems can be built that exhibit intelligent behavior. Cognitive science concerns modeling natural minds, and thus cares most about understanding cognitive processes that yield human thought. Neuroscience concerns the structure and function of brains, and thus cares most about how brains induce minds. Robotics concerns building and directing artificial bodies, and thus cares most about how minds control such bodies.

Will research across these disciplines ultimately converge on a single understanding of mind? This is a deep scientific question to which there is as yet no answer. However, there must at least be a single answer for cognitive science and neuroscience, as they both investigate the same mind, or narrow class of minds, albeit at different levels of abstraction. Research that is inspired by natural systems also may fit within this class of minds, particularly if it is slightly abstracted; but so too may research that has no such aspiration yet still finds itself in the same neighborhood for functional reasons. This broader class comprises what can be called human-like minds.

Our goal with this symposium is to engage the international research community in developing A Common Model of Cognition; that is, a community consensus concerning the mental structures and process implicated in human-like minds to the extent that such a consensus exists. The intent, at least for the foreseeable future, is not to develop a single implementation or model of  cognition by which everyone concerned with human-like cognition would abide, or even a theory in which all of the details are agreed to as correct. What is sought though is a statement of the best consensus given the community’s current understanding of cognition, plus a sound basis for further refinement as more is learned. Much of the existing work on integrative models of cognition focuses on implementations rather than theory, with too little interchange or synthesis possible across these implementations. The development of a common model provides an opportunity for the community to work together at a more abstract level, where such interchange and synthesis should be more practicable.

Earlier work on this topic was under the banner of A Standard Model of the Mind, including (Laird, Lebiere, & Rosenbloom, 2017) and a 2017 AAAI Fall Symposium on “A Standard Model of the Mind.” Since then, this effort has grown into a larger online community, with a change of name that was itself driven by a community consensus, and the creation of online working groups covering the following topics: (1) procedural and working memories; (2) declarative memory; (3) metacognition and reflection; (4) language processing; (5) emotion, mood, affect and motivation; (6) higher-level knowledge, rational and social constraints; (7) lower-level neural and physiological constraints; and (8) perceptual and motor systems. The intent of these working groups is to develop a statement of the best consensus in each area given the community’s current understanding of these components of cognition and how they fit together. The goal of this year’s meeting is to provide a forum to focus on extending the model based on the progress made in the working groups while engaging new participants to the process. Interested people can participate in the effort by subscribing to the Common Model list and joining the working groups of interest. (See https://lists.andrew.cmu.edu/mailman/listinfo/common-model. List archives provide instructions on joining the working groups.)

Laird, J. E., Lebiere, C. & Rosenbloom, P. S. (2017). A Standard Model of the Mind: Toward a Common Computational Framework across Artificial Intelligence, Cognitive Science, Neuroscience, and Robotics. AI Magazine38, 13-26. pdf