Sarath Sreedharan

Plan Explanations as Model Reconciliation: Moving Beyond Explanation as Soliloquy

2017 · arXiv (Cornell University)

When AI systems interact with humans in the loop, they are often called on to provide explanations for their plans and behavior. Past work on plan explanations primarily involved the AI system explaining the correctness …

– D3WA+ – A Case Study of XAIP in a Model Acquisition Task for Dialogue Planning

2020 · Proceedings of the International Conference on Automated Planning and Scheduling

Recently, the D3WA system was proposed as a paradigm shift in how complex goal-oriented dialogue agents can be specified by taking a declarative view of design. However, it turns out actual users of the system …

Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversity

2022 · arXiv (Cornell University)

Creating reinforcement learning (RL) agents that are capable of accepting and leveraging task-specific knowledge from humans has been long identified as a possible strategy for developing scalable approaches for solving long-horizon problems. While previous works …

A Mental-Model Centric Landscape of Human-AI Symbiosis

2022 · arXiv (Cornell University)

There has been significant recent interest in developing AI agents capable of effectively interacting and teaming with humans. While each of these works try to tackle a problem quite central to the problem of human-AI …

On the Planning Abilities of Large Language Models : A Critical Investigation

2023 · arXiv (Cornell University)

Intrigued by the claims of emergent reasoning capabilities in LLMs trained on general web corpora, in this paper, we set out to investigate their planning capabilities. We aim to evaluate (1) the effectiveness of LLMs …