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Found 10 papers, 5 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.
From Reals to Logic and Back: Inventing Symbolic Vocabularies, Actions, and Models for Planning from Raw Data
Empirical results in deterministic settings show that powerful abstract representations can be learned from just a handful of robot trajectories; the learned relational representations include but go beyond classical, intuitive notions of high-level actions; and that the learned models allow planning algorithms to scale to tasks that were previously beyond the scope of planning without hand-crafted abstractions.
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.
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.
Differential Assessment of Black-Box AI Agents
In this work, we propose a novel approach to "differentially" assess black-box AI agents that have drifted from their previously known models.
JEDAI: A System for Skill-Aligned Explainable Robot Planning
This paper presents JEDAI, an AI system designed for outreach and educational efforts aimed at non-AI experts.
Learning Causal Models of Autonomous Agents using Interventions
One of the several obstacles in the widespread use of AI systems is the lack of requirements of interpretability that can enable a layperson to ensure the safe and reliable behavior of such systems.
Discovering User-Interpretable Capabilities of Black-Box Planning Agents
Starting from a set of user-interpretable state properties, an AI agent, and a simulator that the agent can interact with, our algorithm returns a set of high-level capabilities with their parameterized descriptions.
Asking the Right Questions: Learning Interpretable Action Models Through Query Answering
This paper develops a new approach for estimating an interpretable, relational model of a black-box autonomous agent that can plan and act.
