no code implementations • 12 Nov 2023 • Zahra Bashir, Michael Bowling, Levi H. S. Lelis
The LLM then formulates a natural language explanation of the program.
no code implementations • 6 Oct 2023 • Saqib Ameen, Levi H. S. Lelis
In this paper, we show that current state-of-the-art cost-guided BUS algorithms suffer from a common problem: they can lose useful information given by the model and fail to perform the search in a best-first order according to a cost function.
no code implementations • 4 Aug 2023 • Spyros Orfanos, Levi H. S. Lelis
Most of such PIRL algorithms first train a neural policy that is used as an oracle to guide the search in the programmatic space.
1 code implementation • 10 Jul 2023 • Fatemeh Abdollahi, Saqib Ameen, Matthew E. Taylor, Levi H. S. Lelis
Program Optimization with Locally Improving Search (POLIS) exploits the structure of a program, defined by its lines.
1 code implementation • 10 Jul 2023 • Rubens O. Moraes, David S. Aleixo, Lucas N. Ferreira, Levi H. S. Lelis
This paper introduces Local Learner (2L), an algorithm for providing a set of reference strategies to guide the search for programmatic strategies in two-player zero-sum games.
1 code implementation • 26 May 2023 • Laurent Orseau, Marcus Hutter, Levi H. S. Lelis
Levin Tree Search (LTS) is a search algorithm that makes use of a policy (a probability distribution over actions) and comes with a theoretical guarantee on the number of expansions before reaching a goal node, depending on the quality of the policy.
1 code implementation • 10 Aug 2022 • Lucas N. Ferreira, Lili Mou, Jim Whitehead, Levi H. S. Lelis
We use Monte Carlo Tree Search as a decoding mechanism to steer the probability distribution learned by a language model towards a given emotion.
1 code implementation • 22 Mar 2022 • Leandro C. Medeiros, David S. Aleixo, Levi H. S. Lelis
In this paper we show that behavioral cloning can be used to learn effective sketches of programmatic strategies.
1 code implementation • 21 Mar 2021 • Laurent Orseau, Levi H. S. Lelis
LevinTS is guided by a policy and provides guarantees on the number of search steps that relate to the quality of the policy, but it does not make use of a heuristic function.
2 code implementations • 16 Aug 2020 • Lucas N. Ferreira, Levi H. S. Lelis, Jim Whitehead
In this paper we present Bardo Composer, a system to generate background music for tabletop role-playing games.
no code implementations • NeurIPS 2020 • Zaheen Farraz Ahmad, Levi H. S. Lelis, Michael Bowling
Generating good candidate actions is critical to the success of sample-based planners, particularly in continuous or large action spaces.
no code implementations • 5 Apr 2020 • Jonathan Martinez, Kobi Gal, Ece Kamar, Levi H. S. Lelis
AI systems that model and interact with users can update their models over time to reflect new information and changes in the environment.
no code implementations • 30 Jul 2019 • Malte Helmert, Tor Lattimore, Levi H. S. Lelis, Laurent Orseau, Nathan R. Sturtevant
For graph search, A* can require $\Omega(2^{n})$ expansions, where $n$ is the number of states within the final $f$ bound.
no code implementations • 4 Jul 2019 • Dâmaris S. Bento, André G. Pereira, Levi H. S. Lelis
We propose hardness metrics based on pattern database heuristics and the use of novelty to improve the exploration of search methods in the task of generating initial states.
no code implementations • 7 Jun 2019 • Laurent Orseau, Levi H. S. Lelis, Tor Lattimore
Under mild assumptions we prove our algorithms are guaranteed to perform only a logarithmic factor more node expansions than A* when the search space is a tree.
1 code implementation • NeurIPS 2018 • Laurent Orseau, Levi H. S. Lelis, Tor Lattimore, Théophane Weber
We introduce two novel tree search algorithms that use a policy to guide search.
no code implementations • 22 Nov 2017 • Rubens O. Moraes, Levi H. S. Lelis
Action abstractions restrict the number of legal actions available during search in multi-unit real-time adversarial games, thus allowing algorithms to focus their search on a set of promising actions.