7 citations · 15 across the 16 of their papers we have counts for
17 papers
COvolve: Adversarial Co-Evolution of Large-Language-Model-Generated Policies and Environments via Two-Player Zero-Sum Game
Alkis Sygkounas, Rishi Hazra, Andreas Persson +2
A central challenge in building continually improving agents is that training environments are typically static or manually constructed. This restricts continual learning and gener…
APC-RL: Exceeding Data-Driven Behavior Priors with Adaptive Policy Composition
Finn Rietz, Pedro Zuidberg dos Martires, Johannes Andreas Stork
Incorporating demonstration data into reinforcement learning (RL) can greatly accelerate learning, but existing approaches often assume demonstrations are optimal and fully aligned…
LexiCon: a Benchmark for Planning under Temporal Constraints in Natural Language
Periklis Mantenoglou, Rishi Hazra, Pedro Zuidberg Dos Martires +1
Owing to their reasoning capabilities, large language models (LLMs) have been evaluated on planning tasks described in natural language. However, LLMs have largely been tested on p…
A Quantum Information Theoretic Approach to Tractable Probabilistic Models
Pedro Zuidberg Dos Martires
By recursively nesting sums and products, probabilistic circuits have emerged in recent years as an attractive class of generative models as they enjoy, for instance, polytime marg…
Have Large Language Models Learned to Reason? A Characterization via 3-SAT Phase Transition
Rishi Hazra, Gabriele Venturato, Pedro Zuidberg Dos Martires +1
Large Language Models (LLMs) have been touted as AI models possessing advanced reasoning abilities. In theory, autoregressive LLMs with Chain-of-Thought (CoT) can perform more seri…
Automated Reasoning in Systems Biology: a Necessity for Precision Medicine
Pedro Zuidberg Dos Martires, Vincent Derkinderen, Luc De Raedt +1
Recent developments in AI have reinvigorated pursuits to advance the (life) sciences using AI techniques, thereby creating a renewed opportunity to bridge different fields and find…
Can Large Language Models Reason? A Characterization via 3-SAT
Rishi Hazra, Gabriele Venturato, Pedro Zuidberg Dos Martires +1
Large Language Models (LLMs) have been touted as AI models possessing advanced reasoning abilities. However, recent works have shown that LLMs often bypass true reasoning using sho…
REvolve: Reward Evolution with Large Language Models using Human Feedback
Rishi Hazra, Alkis Sygkounas, Andreas Persson +2
Designing effective reward functions is crucial to training reinforcement learning (RL) algorithms. However, this design is non-trivial, even for domain experts, due to the subject…
Probabilistic Neural Circuits
Pedro Zuidberg Dos Martires
Probabilistic circuits (PCs) have gained prominence in recent years as a versatile framework for discussing probabilistic models that support tractable queries and are yet expressi…
Differentiable Sampling of Categorical Distributions Using the CatLog-Derivative Trick
Lennert De Smet, Emanuele Sansone, Pedro Zuidberg Dos Martires
Categorical random variables can faithfully represent the discrete and uncertain aspects of data as part of a discrete latent variable model. Learning in such models necessitates t…
SayCanPay: Heuristic Planning with Large Language Models using Learnable Domain Knowledge
Rishi Hazra, Pedro Zuidberg Dos Martires, Luc De Raedt
Large Language Models (LLMs) have demonstrated impressive planning abilities due to their vast "world knowledge". Yet, obtaining plans that are both feasible (grounded in affordanc…
Top-Down Knowledge Compilation for Counting Modulo Theories
Vincent Derkinderen, Pedro Zuidberg Dos Martires, Samuel Kolb +1
Propositional model counting (#SAT) can be solved efficiently when the input formula is in deterministic decomposable negation normal form (d-DNNF). Translating an arbitrary formul…
Measure Theoretic Weighted Model Integration
Ivan Miosic, Pedro Zuidberg Dos Martires
Weighted model counting (WMC) is a popular framework to perform probabilistic inference with discrete random variables. Recently, WMC has been extended to weighted model integratio…
Symbolic Learning and Reasoning with Noisy Data for Probabilistic Anchoring
Pedro Zuidberg Dos Martires, Nitesh Kumar, Andreas Persson +2
Robotic agents should be able to learn from sub-symbolic sensor data, and at the same time, be able to reason about objects and communicate with humans on a symbolic level. This ra…
Monte Carlo Anti-Differentiation for Approximate Weighted Model Integration
Pedro Zuidberg Dos Martires, Samuel Kolb
Probabilistic inference in the hybrid domain, i.e. inference over discrete-continuous domains, requires tackling two well known #P-hard problems 1)~weighted model counting (WMC) ov…
Learning from Implicit Information in Natural Language Instructions for Robotic Manipulations
Ozan Arkan Can, Pedro Zuidberg Dos Martires, Andreas Persson +5
Human-robot interaction often occurs in the form of instructions given from a human to a robot. For a robot to successfully follow instructions, a common representation of the worl…
Knowledge Compilation with Continuous Random Variables and its Application in Hybrid Probabilistic Logic Programming
Pedro Zuidberg Dos Martires, Anton Dries, Luc De Raedt
In probabilistic reasoning, the traditionally discrete domain has been elevated to the hybrid domain encompassing additionally continuous random variables. Inference in the hybrid…