“A Competence Framework for Artificial Intelligence Research”
Abstract: While over the last few decades AI research has largely focused on building tools and applications, recent technological developments have prompted a resurgence of interest in building a genuinely intelligent artificial agent—one that has a mind in the same sense that humans and animals do. In this paper I offer a theoretical and methodological framework for this project of investigating “artificial minded intelligence” (AMI) that can help to unify existing approaches and provide new avenues for research. I first motivate three desiderata that a framework for AMI research should satisfy, and explain why existing AI research does not adequately do so. I then develop a general methodological approach that satisfies these desiderata. According to the generative methodology, we should divide the explanatory task into the development of three coordinated models (i) an agent model: a nonreductive characterization of the intelligent behavior to be explained that facilitates hypothesis development and measurement across a variety of contexts, (ii) a basis model: a characterization of the artificial system in computational, mechanical, and/or behavioral terms, and (iii) a generative model: a model of how changes in basis features make differences to, or determine, features of the agent. I then augment the view by providing a competence framework for agent models and show how it can help us to illuminate key features of interest in AI research, such as robustness, flexibility, and autonomy.
This seminar is relevant to the Philosophy-Neuroscience-Psychology (PNP) program and is hosted by the Department of Philosophy.
For inquiries contact Sue McKinney.