Goal recognition via model-based and model-free techniques

TL;DR

This paper compares model-based and model-free goal recognition techniques, adapting state-of-the-art learning methods and analyzing their effectiveness in various domains, highlighting their potential in financial applications.

Contribution

It introduces the adaptation of advanced learning techniques for goal recognition and provides a comparative analysis with planning-based methods in different domains.

Findings

Planning-based approaches are suitable for certain financial goal-recognition tasks.

Model-free methods show promise but require further adaptation for finance.

Trade-offs between accuracy and computational efficiency are discussed.

Abstract

Goal recognition aims at predicting human intentions from a trace of observations. This ability allows people or organizations to anticipate future actions and intervene in a positive (collaborative) or negative (adversarial) way. Goal recognition has been successfully used in many domains, but it has been seldom been used by financial institutions. We claim the techniques are ripe for its wide use in finance-related tasks. The main two approaches to perform goal recognition are model-based (planning-based) and model-free (learning-based). In this paper, we adapt state-of-the-art learning techniques to goal recognition, and compare model-based and model-free approaches in different domains. We analyze the experimental data to understand the trade-offs of using both types of methods. The experiments show that planning-based approaches are ready for some goal-recognition finance tasks.