13 citations · 13 across the 16 of their papers we have counts for
17 papers
LeanPlan: Optimal Planning with LLM-Generated Heuristics and Admissibility Proofs
André G. Pereira, Augusto B. Corrêa, Felipe Meneguzzi +1
Frontier large language models (LLMs) can generate heuristic functions that guide search to achieve state-of-the-art performance in satisficing planning, where any plan is acceptab…
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…
Automated Planning for Optimal Data Pipeline Instantiation
Leonardo Rosa Amado, Adriano Vogel, Dalvan Griebler +3
Data pipeline frameworks provide abstractions for implementing sequences of data-intensive transformation operators, automating the deployment and execution of such transformations…
Intention Recognition in Real-Time Interactive Navigation Maps
Peijie Zhao, Zunayed Arefin, Felipe Meneguzzi +1
In this demonstration, we develop IntentRec4Maps, a system to recognise users' intentions in interactive maps for real-world navigation. IntentRec4Maps uses the Google Maps Platfor…
Goal Recognition using Actor-Critic Optimization
Ben Nageris, Felipe Meneguzzi, Reuth Mirsky
Goal Recognition aims to infer an agent's goal from a sequence of observations. Existing approaches often rely on manually engineered domains and discrete representations. Deep Rec…
Goal Recognition via Linear Programming
Felipe Meneguzzi, Luísa R. de A. Santos, Ramon Fraga Pereira +1
Goal Recognition is the task by which an observer aims to discern the goals that correspond to plans that comply with the perceived behavior of subject agents given as a sequence o…
Real-time goal recognition using approximations in Euclidean space
Douglas Tesch, Leonardo Rosa Amado, Felipe Meneguzzi
While recent work on online goal recognition efficiently infers goals under low observability, comparatively less work focuses on online goal recognition that works in both discret…
Temporally Extended Goal Recognition in Fully Observable Non-Deterministic Domain Models
Ramon Fraga Pereira, Francesco Fuggitti, Felipe Meneguzzi +1
Goal Recognition is the task of discerning the correct intended goal that an agent aims to achieve, given a set of goal hypotheses, a domain model, and a sequence of observations (…
Self-Supervised Adversarial Imitation Learning
Juarez Monteiro, Nathan Gavenski, Felipe Meneguzzi +1
Behavioural cloning is an imitation learning technique that teaches an agent how to behave via expert demonstrations. Recent approaches use self-supervision of fully-observable unl…
Goal Recognition as Reinforcement Learning
Leonardo Rosa Amado, Reuth Mirsky, Felipe Meneguzzi
Most approaches for goal recognition rely on specifications of the possible dynamics of the actor in the environment when pursuing a goal. These specifications suffer from two key…
CP-MDP: A CANDECOMP-PARAFAC Decomposition Approach to Solve a Markov Decision Process Multidimensional Problem
Daniela Kuinchtner, Afonso Sales, Felipe Meneguzzi
Markov Decision Process (MDP) is the underlying model for optimal planning for decision-theoretic agents in stochastic environments. Although much research focuses on solving MDP p…
Inferring Agents Preferences as Priors for Probabilistic Goal Recognition
Kin Max Gusmão, Ramon Fraga Pereira, Felipe Meneguzzi
Recent approaches to goal recognition have leveraged planning landmarks to achieve high-accuracy with low runtime cost. These approaches, however, lack a probabilistic interpretati…