Debugging Agent Programs with Why?: Questions
By Michael Winikoff. 2017
38 citations recorded by the source corpus.
Debugging is hard, and debugging cognitive agent programs is particularly hard, since they involve concurrency, a dynamic environment, and a complex execution model that includes failure handling. Previous work by Ko & Myers has demonstrated that providing Alice and Java programmers with software that can answer "why?" and "why not?" questions can make a dramatic difference to debugging performance. This paper considers how to adapt this approach to cognitive agent programs, specifically AgentSpeak. It develops and formalises definitions for "why?" and "why not?" questions and associated answers, and illustrates their application using a scenario.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- Tim Miller
- Liz Sonenberg
- Prashan Madumal
- Frank Vetere
- Peter A. Flach
- Kacper Sokol
- Longo Luca
- Giulia Vilone
Citations in the corpus
- Explanation in Artificial Intelligence: Insights from the Social Sciences (2017, background): “For example, Winikoff [189] considers the questions of “Why don’t you believe .”
- A Grounded Interaction Protocol for Explainable Artificial Intelligence (2019, background): “Most modern work on Explainable AI, such as in autonomous agents [5, 7, 15, 36] and interpretable machine learning [13], does not discuss the interaction and the social aspect of the explanations.”