# Goal Recognition Design for General Behavioral Agents using Machine Learning

arXiv: https://arxiv.org/abs/2404.03054 · HTML: https://mlsanity.com/paper/2404.03054/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 84 references that are in the map.
- First posted on arXiv 3 Apr 2024.

## Builds on (most cited first, 23 of 23)

- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [On the Utility of Learning about Humans for Human-AI Coordination](https://mlsanity.com/paper/1910.05789/) (2019)
- [On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inference](https://mlsanity.com/paper/1906.09624/) (2019)
- [Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems](https://mlsanity.com/paper/1405.2875/) (2014)
- [Learning the Preferences of Ignorant, Inconsistent Agents](https://mlsanity.com/paper/1512.05832/) (2015)
- [When Humans Aren't Optimal: Robots that Collaborate with Risk-Aware Humans](https://mlsanity.com/paper/2001.04377/) (2020)
- [Auction learning as a two-player game](https://mlsanity.com/paper/2006.05684/) (2020)
- [Human irrationality: both bad and good for reward inference](https://mlsanity.com/paper/2111.06956/) (2021)
- [PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learning](https://mlsanity.com/paper/2106.03215/) (2021)
- [On the Utility of Model Learning in HRI](https://mlsanity.com/paper/1901.01291/) (2019)
- [Certifying Strategyproof Auction Networks](https://mlsanity.com/paper/2006.08742/) (2020)
- [ProportionNet: Balancing Fairness and Revenue for Auction Design with Deep Learning](https://mlsanity.com/paper/2010.06398/) (2020)
- [Incentivizing High Quality Crowdwork](https://mlsanity.com/paper/1503.05897/) (2015)
- [Performative Prediction with Bandit Feedback: Learning through Reparameterization](https://mlsanity.com/paper/2305.01094/) (2023)
- [Bandit Learning with Delayed Impact of Actions](https://mlsanity.com/paper/2002.10316/) (2020)
- [Assisted Perception: Optimizing Observations to Communicate State](https://mlsanity.com/paper/2008.02840/) (2020)
- [Rationality-Robust Information Design: Bayesian Persuasion under Quantal Response](https://mlsanity.com/paper/2207.08253/) (2022)
- [Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members](https://mlsanity.com/paper/2208.08798/) (2022)
- [Competitive Information Design for Pandora's Box](https://mlsanity.com/paper/2103.03769/) (2021)
- [Low-Cost Learning via Active Data Procurement](https://mlsanity.com/paper/1502.05774/) (2015)
- [Distributional Latent Variable Models with an Application in Active Cognitive Testing](https://mlsanity.com/paper/2312.09316/) (2023)
- [Optimal Query Complexity of Secure Stochastic Convex Optimization](https://mlsanity.com/paper/2104.01926/) (2021)
- [Generalising Planning Environment Redesign](https://mlsanity.com/paper/2402.07799/) (2024)

## Built on by (most cited first, 2 of 2)

- [On the Utility of Accounting for Human Beliefs about AI Intention in Human-AI Collaboration](https://mlsanity.com/paper/2406.06051/) (2024)
- [Early Prediction of Future Behavioral Strategy from Process Traces](https://mlsanity.com/paper/2605.30550/) (2026)

## Abstract (arXiv)

Goal recognition design (GRD) aims to make limited modifications to decision-making environments to make it easier to infer the goals of agents acting within those environments. Although various research efforts have been made in goal recognition design, existing approaches are computationally demanding and often assume that agents are (near-)optimal in their decision-making. To address these limitations, we leverage machine learning methods for goal recognition design that can both improve run-time efficiency and account for agents with general behavioral models. Following existing literature, we use worst-case distinctiveness (wcd) as a measure of the difficulty in inferring the goal of an agent in a decision-making environment. Our approach begins by training a machine learning model to predict the wcd for a given environment and the agent behavior model. We then propose a gradient-based optimization framework that accommodates various constraints to optimize decision-making environments for enhanced goal recognition. Through extensive simulations, we demonstrate that our approach outperforms existing methods in reducing wcd and enhances runtime efficiency. Moreover, our approach also adapts to settings in which existing approaches do not apply, such as those involving flexible budget constraints, more complex environments, and suboptimal agent behavior. Finally, we conducted human-subject experiments that demonstrate that our method creates environments that facilitate efficient goal recognition from human decision-makers.