131,031 research outputs found
The structure and formation of natural categories
Categorization and concept formation are critical activities of intelligence. These processes and the conceptual structures that support them raise important issues at the interface of cognitive psychology and artificial intelligence. The work presumes that advances in these and other areas are best facilitated by research methodologies that reward interdisciplinary interaction. In particular, a computational model is described of concept formation and categorization that exploits a rational analysis of basic level effects by Gluck and Corter. Their work provides a clean prescription of human category preferences that is adapted to the task of concept learning. Also, their analysis was extended to account for typicality and fan effects, and speculate on how the concept formation strategies might be extended to other facets of intelligence, such as problem solving
Recognising the importance of preference change: A call for a coordinated multidisciplinary research effort in the age of AI
As artificial intelligence becomes more powerful and a ubiquitous presence in
daily life, it is imperative to understand and manage the impact of AI systems
on our lives and decisions. Modern ML systems often change user behavior (e.g.
personalized recommender systems learn user preferences to deliver
recommendations that change online behavior). An externality of behavior change
is preference change. This article argues for the establishment of a
multidisciplinary endeavor focused on understanding how AI systems change
preference: Preference Science. We operationalize preference to incorporate
concepts from various disciplines, outlining the importance of meta-preferences
and preference-change preferences, and proposing a preliminary framework for
how preferences change. We draw a distinction between preference change,
permissible preference change, and outright preference manipulation. A
diversity of disciplines contribute unique insights to this framework.Comment: Accepted at the AAAI-22 Workshop on AI For Behavior Change held at
the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22), 7
pages, 1 figur
Social Choice Optimization
Social choice is the theory about collective decision towards social welfare
starting from individual opinions, preferences, interests or welfare. The field
of Computational Social Welfare is somewhat recent and it is gaining impact in
the Artificial Intelligence Community. Classical literature makes the
assumption of single-peaked preferences, i.e. there exist a order in the
preferences and there is a global maximum in this order. This year some
theoretical results were published about Two-stage Approval Voting Systems
(TAVs), Multi-winner Selection Rules (MWSR) and Incomplete (IPs) and Circular
Preferences (CPs). The purpose of this paper is three-fold: Firstly, I want to
introduced Social Choice Optimisation as a generalisation of TAVs where there
is a max stage and a min stage implementing thus a Minimax, well-known
Artificial Intelligence decision-making rule to minimize hindering towards a
(Social) Goal. Secondly, I want to introduce, following my Open Standardization
and Open Integration Theory (in refinement process) put in practice in my
dissertation, the Open Standardization of Social Inclusion, as a global social
goal of Social Choice Optimization
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