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Osher Elhadad

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2 papers

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AAMAS Conference 2026 Conference Paper

General Dynamic Goal Recognition using Goal-Conditioned and Meta Reinforcement Learning

Osher Elhadad
Owen Morrissey
Reuth Mirsky

Understanding an agent’s goal through its behavior is a common AI problem called Goal Recognition (GR). This task becomes particularly challenging in dynamic environments where goals are numerousandever-changing. Weintroducethe GeneralDynamic Goal Recognition (GDGR) problem, a broader definition of GR aimed at real-time adaptation of GR systems. This paper presents two novel approaches to tackle GDGR: (1) GC-AURA, generalizing to new goals using Goal-Conditioned Reinforcement Learning, and (2) Meta-AURA, adapting to novel environments with Meta- Reinforcement Learning. We evaluate these methods across diverse environments, demonstrating their ability to achieve rapid adaptation and high GR accuracy under dynamic and noisy conditions. This work is a significant step forward in enabling GR in dynamic and unpredictable real-world environments.

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AAMAS Conference 2026 Conference Paper

GRAIL: Goal Recognition Alignment through Imitation Learning

Osher Elhadad
Felipe Meneguzzi
Reuth Mirsky

Aligning AI systems with human intentions requires understanding an agent’s goals from its behavior. Existing goal recognition methods typically rely on an approximately optimal goal-oriented policy representation, which may differ from the actor’s true behavior and hinder the accurate recognition of their goal. To address this gap, this paper introduces Goal Recognition Alignment through ImitationLearning(GRAIL), whichleveragesimitationlearningand inverse reinforcement learning to learn one goal-directed policy for each candidate goal directly from (potentially suboptimal) demonstration trajectories. By scoring an observed partial trajectory with each learned goal-directed policy in a single forward pass, GRAIL retains the one-shot inference capability of classical goal recognition while leveraging learned policies that can capture suboptimal and systematically biased behavior. Empirical evaluations show that GRAIL outperforms standard reinforcement learning-based GRtechniquesinrecognizingbothsuboptimalandbiasedbehaviors in controlled, closed-set goal environments.

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