André Grahl Pereira

Felipe Meneguzzi, Alexandre Buchweitz, Augusto B. Corrêa et al.

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 until only executable actions remain. On one hand, this allows one to introduce domain knowledge that can speed up the search for a solution through the method library. On the other hand, it creates challenges that go beyond those of classical state-space search. While recent research produced a number of heuristics and novel algorithms that speed up HTN planning, these heuristics are not yet as informative as those available in classical planning algorithms. We investigate whether large language models (LLMs) can generate effective search heuristics for HTN planning, extending the methodology of Corrêa, Pereira, and Seipp (2025) from classical to hierarchical planning. Using the Pytrich planner on six standard total-order HTN benchmark domains, we evaluate heuristics generated by nine LLMs under domain-specific prompting and compare them against the TDG and LMCount domain-independent baselines and the PANDA planner. Our results show that LLM-generated heuristics nearly match the coverage of the best available HTN planner, while substantially reducing search effort on 83% of shared problems.

Frederico Messa, André Grahl Pereira

Fully-observable non-deterministic (FOND) planning is at the core of artificial intelligence planning with uncertainty. It models uncertainty through actions with non-deterministic effects. A* with Non-Determinism (AND*) (Messa and Pereira, 2023) is a FOND planner that generalizes A* (Hart et al., 1968) for FOND planning. It searches for a solution policy by performing an explicit heuristic search on the policy space of the FOND task. In this paper, we study and improve the performance of the policy-space search performed by AND*. We present a polynomial-time procedure that constructs a solution policy given just the set of states that should be mapped. This procedure, together with a better understanding of the structure of FOND policies, allows us to present three concepts of equivalences between policies. We use policy equivalences to prune part of the policy search space, making AND* substantially more effective in solving FOND tasks. We also study the impact of taking into account structural state-space symmetries to strengthen the detection of equivalence policies and the impact of performing the search with satisficing techniques. We apply a recent technique from the group theory literature to better compute structural state-space symmetries. Finally, we present a solution compressor that, given a policy defined over complete states, finds a policy that unambiguously represents it using the minimum number of partial states. AND* with the introduced techniques generates, on average, two orders of magnitude fewer policies to solve FOND tasks. These techniques allow explicit policy-space search to be competitive in terms of both coverage and solution compactness with other state-of-the-art FOND planners.

Luísa R. de A. Santos, Felipe Meneguzzi, Ramon Fraga Pereira et al.

Goal recognition aims to recognize the set of candidate goals that are compatible with the observed behavior of an agent. In this paper, we develop a method based on the operator-counting framework that efficiently computes solutions that satisfy the observations and uses the information generated to solve goal recognition tasks. Our method reasons explicitly about both partial and noisy observations: estimating uncertainty for the former, and satisfying observations given the unreliability of the sensor for the latter. We evaluate our approach empirically over a large data set, analyzing its components on how each can impact the quality of the solutions. In general, our approach is superior to previous methods in terms of agreement ratio, accuracy, and spread. Finally, our approach paves the way for new research on combinatorial optimization to solve goal recognition tasks.