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Using Awareness to Promote Richer, More Human-Like Behaviors in Artificial Agents

  • Logan YliniemiEmail author
  • Kagan Tumer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10003)

Abstract

The agents community has produced a wide variety of compelling solutions for many real-world problems, and yet there is still a significant disconnect between the behaviors that an agent can learn and those that exemplify the rich behaviors exhibited by humans. This problem exists both with agents interacting solely with an environment, as well as agents interacting with other agents. The solutions created to date are typically good at solving a single, well-defined problem with a particular objective, but lack in generalizability.

In this work, we discuss the possibility of using an awareness framework, coupled with the optimization of multiple dynamic objectives, in tandem with the cooperation and coordination concerns intrinsic to multiagent systems, to create a richer set of agent behaviors. We propose future directions of research that may lead toward more-human capabilities in general agent behaviors.

Keywords

Indifference Curve Context Switch Human Decision Making Process Complex Optimization Problem Preference Curve 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.University of Nevada, RenoRenoUSA
  2. 2.Oregon State UniversityCorvallisUSA

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