A Knowledge Driven Approach to Adaptive Assistance Using Preference Reasoning and Explanation

2020-12-05 · Jason R. Wilson, Leilani Gilpin, Irina Rabkina

There is a need for socially assistive robots (SARs) to provide transparency in their behavior by explaining their reasoning. Additionally, the reasoning and explanation should represent the user's preferences and goals. To work towards satisfying this need for interpretable reasoning and representations, we propose the robot uses Analogical Theory of Mind to infer what the user is trying to do and uses the Hint Engine to find an appropriate assistance based on what the user is trying to do. If the user is unsure or confused, the robot provides the user with an explanation, generated by the Explanation Synthesizer. The explanation helps the user understand what the robot inferred about the user's preferences and why the robot decided to provide the assistance it gave. A knowledge-driven approach provides transparency to reasoning about preferences, assistance, and explanations, thereby facilitating the incorporation of user feedback and allowing the robot to learn and adapt to the user.

📄 PDF Abstract BibTeX arXiv:2012.02904

Code (0)

등록된 구현이 없습니다.

Methods 이 논문이 사용한 방법론

Attention 설명 없음

Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…

Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.

Multi-Head Attention 설명 없음

Synthesizer 설명 없음

Learn and Transfer Knowledge of Preferred Assistance Strategies in Semi-autonomous Telemanipulation

2020-03-07 · Lingfeng Tao, Michael Bowman, Xu Zhou, Jiucai Zhang 외

Enabling robots to provide effective assistance yet still accommodating the operator's commands for telemanipulation of an object is very challenging because robot's assistive action is not always intuitive for human ope…

Transfer Learning

Towards Multi-Source Retrieval-Augmented Generation via Synergizing Reasoning and Preference-Driven Retrieval

2024-11-01 · Qingfei Zhao, Ruobing Wang, Xin Wang, Daren Zha 외

Retrieval-Augmented Generation (RAG) has emerged as a reliable external knowledge augmentation technique to mitigate hallucination issues and parameterized knowledge limitations in Large Language Models (LLMs). Existing …

Hallucination RAG Retrieval Retrieval-augmented Generation

RPRO: Ranked Preference Reinforcement Optimization for Enhancing Medical QA and Diagnostic Reasoning

2025-08-31 · Chia-Hsuan Hsu, Jun-En Ding, Hsin-Ling Hsu, Chih-Ho Hsu 외 arxiv

Medical question answering requires advanced reasoning that integrates domain knowledge with logical inference. However, existing large language models (LLMs) often generate reasoning chains that lack factual accuracy an…

Reinforcement Learning Question Answering

UserCentrix: An Agentic Memory-augmented AI Framework for Smart Spaces

2025-05-01 · Alaa Saleh, Sasu Tarkoma, Praveen Kumar Donta, Naser Hossein Motlagh 외

Agentic AI, with its autonomous and proactive decision-making, has transformed smart environments. By integrating Generative AI (GenAI) and multi-agent systems, modern AI frameworks can dynamically adapt to user preferen…

Decision Making Large Language Model Management

Agentic Personas for Adaptive Scientific Explanations with Knowledge Graphs

2026-03-23 · Susana Nunes, Tiago Guerreiro, Catia Pesquita arxiv

AI explanation methods often assume a static user model, producing non-adaptive explanations regardless of expert goals, reasoning strategies, or decision contexts. Knowledge graph-based explanations, despite their capac…

Reinforcement Learning Explanation Generation Knowledge Graphs Drug Discovery