Tutorial on Predicting Human Decision-Making: Tools of the Trade

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Human decision-making often transcends our formal models of ``rationality". Designing intelligent agents that interact proficiently with people necessitates the modeling of human behavior and the prediction of their decisions.

In this 3.5 hour tutorial, we will focus on the prediction of human decision-making and its use in designing intelligent human-aware automated agents of varying natures; from purely conflicting interaction settings (e.g., security and games) to fully cooperative interaction settings (e.g., advise provision, human rehabilitation). We will present computational representations, algorithms and empirical methodologies for meeting the challenges that arise from the above tasks in both a single interaction (one-shot) and repeated interaction settings. The tutorial will also review recent advances, current challenges and future directions for the field.


In the course of the tutorial we will present techniques and ideas using machine learning, game-theoretical and general AI concepts. The basis for these concepts will be covered as part of the tutorial, however, a basic familiarity with the above concepts is encouraged.

Tutorial Outline


  • Motivation and examples: Agents for human rehabilitation, human-robot interaction, automated advice provision, argumentation, security, negotiation etc.
  • The basics of human decision-making: decision theory and game theory, bounded rationality, historical and contemporary computational models of human behavior from behavioral economics, cognitive psychology and AI. Experimental evidence for human decision-making and behavior and they are different from what we usually consider to be rational.
  • Tools of the trade: Normative vs. descriptive approaches for predicting human decision-making, computational models and the integration of normative theories from different disciplines (e.g., social science and economics) to enhance classic prediction methods.
  • From prediction to action: combining human decision-making prediction methods in the design of intelligent agents that interact proficiently with people. Frameworks, methodologies and applications to security, games, argumentation, advice provision, rehabilitation, human-robot interaction, personal assistants and negotiation.
  • Additional topics and challenges: implicit vs. explicit interaction settings, enhancing prediction capabilities using additional modalities (e.g., facial expressions), transfer learning of decision policies across domains and people, the complexity of acquiring (reliable) human data, minority cases in human-generated data.

Tutorial Materials

Slides.

References.

Ariel Rosenfeld

Ariel Rosenfeld is a Koshland Postdoctoral Fellow at the Department of Computer Science and Applied Mathematics at Weizmann Institute of Science, Israel. He obtained a BSc in Computer Science and Economics, graduated `magna cum laude', from Tel-Aviv University, Israel and a PhD in Computer Science form Bar-Ilan University, Israel. Rosenfeld's research focus is Human-Agent Interaction and he has published on the topic at top venues such as AAAI, IJCAI, AAMAS and ECAI. Rosenfeld has a rich lecturing background, spanning over a decade, and he is currently acting as a lecturer at Bar-Ilan University, Israel.


arielros1 at gmail dot com

Department of Computer Science and Applied Mathematics

Weizmann Institute of Science, Israel

www.arielrosenfeld.com