Theoretical Framework for Interaction Game Design

Abstract

The PI aimed at establishing a theoretical framework for building a common ground that may allow for content-rich, proficient and reliable communication between people and robots. To solicit high quality and spontaneous contribution from people, the PI needed to make the process of common ground acquisition as playful as possible, by incorporating aspects of game, story and play. The notion of interaction game is critical to induce a strong engagement of participants. This was addressed through learning from demonstration for acquiring interaction patterns, cognitive aspects of interaction game and an integrated framework for interaction game design. Major results encompass a robust learning algorithm from demonstration SAX Imitate, an integrated toolbox MC2 (Motif Change and Causality Discovery), implementation and evaluation of a virtual basketball game, investigation of the effect of back imitation, a method for inducing intentional stance in HAI (Human-Agent Interaction), using physiological indices for discriminating intrinsic and extrinsic stress, and SES (Synthetic Evidential Study).

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Document Details

Document Type
Technical Report
Publication Date
May 19, 2016
Accession Number
AD1032938

Entities

People

  • Toyoaki Nishida

Organizations

  • Kyoto University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Artificial Intelligence
  • Cognitive Science
  • Cognitive Systems Engineering
  • Computer Science
  • Computers
  • Databases
  • Human Behavior
  • Human-Computer Interaction
  • Human-Robot Interaction
  • Image Processing
  • Information Systems
  • Machine Learning
  • Signal Processing
  • Virtual Reality

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Distributed Systems and Data Platform Development
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • Autonomy
  • Autonomy - Human-Robot Interaction