How the Experts Do It: Assessing and Explaining Agent Behaviors in Real-Time Strategy Games

By Jonathan Dodge, Sean Penney, Claudia Hilderbrand, Andrew Anderson, Margaret M. Burnett. 2017

34 citations recorded by the source corpus.

How should an AI-based explanation system explain an agent's complex behavior to ordinary end users who have no background in AI? Answering this question is an active research area, for if an AI-based explanation system could effectively explain intelligent agents' behavior, it could enable the end users to understand, assess, and appropriately trust (or distrust) the agents attempting to help them. To provide insights into this question, we turned to human expert explainers in the real-time strategy domain --"shoutcasters"-- to understand (1) how they foraged in an evolving strategy game in real time, (2) how they assessed the players' behaviors, and (3) how they constructed pertinent and timely explanations out of their insights and delivered them to their audience. The results provided insights into shoutcasters' foraging strategies for gleaning information necessary to assess and explain the players; a characterization of the types of implicit questions shoutcasters answered; and implications for creating explanations by using the patterns and abstraction levels these human experts revealed.

Subfields: Artificial Intelligence, Information Systems and Management

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. Shane T. Mueller
  2. Gary Klein
  3. Robert R. Hoffman
  4. William J. Clancey
  5. Abigail Emrey
  6. D. Gunning
  7. David William Aha
  8. Kejkaew Thanasuan

In Information Systems and Management

  1. Gary Klein
  2. Robert R. Hoffman
  3. William J. Clancey
  4. Abigail Emrey
  5. Shane T. Mueller

Citations in the corpus