Towards Computational Modeling of Human Goal Recognition

Abstract

Recently, we are seeing the emergence of plan- and goal-recognition algorithms which are based on the principle of rationality. These avoid the use of a plan library that compactly encodes all possible observable plans, and instead generate plans dynamically to match the observations. However, recent experiments by Berkovitz (2018) show that in many cases, humans seem to have reached quick (correct) decisions when observing motions which were far from rational (optimal), while optimal motions were slower to be recognized. Intrigued by these findings, we experimented with a variety of rationality-based recognition algorithms on the same data. The results clearly show that none of the algorithms reported in the literature accounts for human subject decisions, even in this simple task. This is our first contribution. We then investigate the novel algorithm on a different domain of shape recognition based on a work by Vered et al (2016). They contrasted human behavior in recognizing hand-drawn shapes with an algorithm based on the rationality assumption. Here we show that our proposed novel algorithm accounts for human results from the shape recognition domain slightly better than the existing algorithms.

Date of Award	2022
Original language	American English
Supervisor	Gal Kaminka (Supervisor)