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Found 7 papers, 2 papers with code
Can LLMs Converse Formally? Automatically Assessing LLMs in Translating and Interpreting Formal Specifications
Stakeholders often describe system requirements using natural language which are then converted to formal syntax by a domain-expert leading to increased design costs.
Epistemic Exploration for Generalizable Planning and Learning in Non-Stationary Settings
This paper introduces a new approach for continual planning and model learning in non-stationary stochastic environments expressed using relational representations.
Autonomous Capability Assessment of Sequential Decision-Making Systems in Stochastic Settings (Extended Version)
It is essential for users to understand what their AI systems can and can't do in order to use them safely.
Relational Abstractions for Generalized Reinforcement Learning on Symbolic Problems
Reinforcement learning in problems with symbolic state spaces is challenging due to the need for reasoning over long horizons.
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
This large and diverse collection of tasks enables rigorous benchmarking of cross-task generalization under instructions -- training models to follow instructions on a subset of tasks and evaluating them on the remaining unseen ones.
Learning Generalized Policy Automata for Relational Stochastic Shortest Path Problems
Several goal-oriented problems in the real-world can be naturally expressed as Stochastic Shortest Path Problems (SSPs).
Learning Generalized Relational Heuristic Networks for Model-Agnostic Planning
Computing goal-directed behavior is essential to designing efficient AI systems.
