Act-Now: Autonomous Cognitive Technologies for Novelty in Open Worlds

Abstract

The main objective of the project was to develop a versatile novelty-aware cognitive robotic architecture that will enable artificial agents to cope with "unknown unknowns". Specifically, we aimed to develop inference-based and learning-based methods for novelty detection, combining statistically trained sub symbolic models with knowledge-based symbolic models and utilizing a mixture of symbolic and statistical inference for detecting deviations from expectations. We used three TA1 tasks to develop these algorithms: Polycraft, Monopoly, and NLP (the last was removed in the final phase of the project). Another main goal was to also characterize the detected novelties and determine how to cope with them based on the effects they had on the agent's task. We also planned to integrate all algorithms for novelty detection, characterization, and accommodation into the unified architecture framework to be able to utilize them in future work in different domains.

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Document Details

Document Type
Technical Report
Publication Date
Jul 11, 2023
Accession Number
AD1225550

Entities

People

  • Chitta Baral
  • Jivko Sinapov
  • Liping Liu
  • Matthias J Scheutz
  • Michael C. Hughes
  • Subbarao Kambhampati

Organizations

  • Tufts University

Tags

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Neural Network Machine Learning.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • Autonomy
  • Autonomy - Autonomous System Control