9 papers
Multi-Agent Goal Recognition with Team- and Goal-Conditioned Reinforcement Learning and Factorized Branch-and-Bound
Thiago Thomas, Gabriel de Oliveira Ramos, Felipe Meneguzzi
Multi-agent goal recognition asks an observer to jointly infer which agents act together and what each team is trying to achieve, so the hypothesis space grows combinatorially with…
Hierarchical Task Network Planning with LLM-Generated Heuristics
Felipe Meneguzzi, Alexandre Buchweitz, Augusto B. Corrêa +2
HTN planning is a variation of classical planning where, instead of searching for a linear sequence of actions, an algorithm decomposes higher-level tasks using a method library un…
Zero-Shot Goal Recognition with Large Language Models
Kin Max Piamolini Gusmão, Nathan Gavenski, Nir Oren +1
Large language models have recently reached near-parity with classical planners on well-known planning domains, yet this competence relies on world-knowledge exploitation rather th…
Online Goal Recognition using Path Signature and Dynamic Time Warping
Douglas Tesch, Nathan Gavenski, Leonardo Amado +2
Online goal recognition in continuous domains poses two central challenges: efficiently encoding large trajectories and effectively comparing them. Recent work addresses these chal…
Beyond Mimicry: Toward Lifelong Adaptability in Imitation Learning
Nathan Gavenski, Felipe Meneguzzi, Odinaldo Rodrigues
Imitation learning stands at a crossroads: despite decades of progress, current imitation learning agents remain sophisticated memorisation machines, excelling at replay but failin…
GRAIL: Goal Recognition Alignment through Imitation Learning
Osher Elhadad, Felipe Meneguzzi, Reuth Mirsky
Understanding an agent's goals from its behavior is fundamental to aligning AI systems with human intentions. Existing goal recognition methods typically rely on an optimal goal-or…