Towards Teachable Autotelic Agents

• URL: http://arxiv.org/abs/2105.11977v3
• Date: Mon, 20 Mar 2023 15:24:01 GMT
• Title: Towards Teachable Autotelic Agents
• Authors: Olivier Sigaud and Ahmed Akakzia and Hugo Caselles-Dupr\'e and C\'edric Colas and Pierre-Yves Oudeyer and Mohamed Chetouani
• Abstract summary: Teachable autotelic agents (TAA) are agents that learn from both internal and teaching signals. This paper presents a roadmap towards the design of teachable autonomous agents.
• Score: 21.743801780657435
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous discovery and direct instruction are two distinct sources of learning in children but education sciences demonstrate that mixed approaches such as assisted discovery or guided play result in improved skill acquisition. In the field of Artificial Intelligence, these extremes respectively map to autonomous agents learning from their own signals and interactive learning agents fully taught by their teachers. In between should stand teachable autotelic agents (TAA): agents that learn from both internal and teaching signals to benefit from the higher efficiency of assisted discovery. Designing such agents will enable real-world non-expert users to orient the learning trajectories of agents towards their expectations. More fundamentally, this may also be a key step to build agents with human-level intelligence. This paper presents a roadmap towards the design of teachable autonomous agents. Building on developmental psychology and education sciences, we start by identifying key features enabling assisted discovery processes in child-tutor interactions. This leads to the production of a checklist of features that future TAA will need to demonstrate. The checklist allows us to precisely pinpoint the various limitations of current reinforcement learning agents and to identify the promising first steps towards TAA. It also shows the way forward by highlighting key research directions towards the design or autonomous agents that can be taught by ordinary people via natural pedagogy.

Related papers

• Evolution of AI in Education: Agentic Workflows [2.1681971652284857]
  Artificial intelligence (AI) has transformed various aspects of education. Large language models (LLMs) are driving advancements in automated tutoring, assessment, and content generation. To address these limitations and foster more sustainable technological practices, AI agents have emerged as a promising new avenue for educational innovation.
  arXiv  Detail & Related papers  (2025-04-25T13:44:57Z)
• Memento No More: Coaching AI Agents to Master Multiple Tasks via Hints Internalization [56.674356045200696]
  We propose a novel method to train AI agents to incorporate knowledge and skills for multiple tasks without the need for cumbersome note systems or prior high-quality demonstration data. Our approach employs an iterative process where the agent collects new experiences, receives corrective feedback from humans in the form of hints, and integrates this feedback into its weights. We demonstrate the efficacy of our approach by implementing it in a Llama-3-based agent which, after only a few rounds of feedback, outperforms advanced models GPT-4o and DeepSeek-V3 in a taskset.
  arXiv  Detail & Related papers  (2025-02-03T17:45:46Z)
• LLM-powered Multi-agent Framework for Goal-oriented Learning in Intelligent Tutoring System [54.71619734800526]
  GenMentor is a multi-agent framework designed to deliver goal-oriented, personalized learning within ITS. It maps learners' goals to required skills using a fine-tuned LLM trained on a custom goal-to-skill dataset. GenMentor tailors learning content with an exploration-drafting-integration mechanism to align with individual learner needs.
  arXiv  Detail & Related papers  (2025-01-27T03:29:44Z)
• Generative AI in Education: From Foundational Insights to the Socratic Playground for Learning [2.8947618493306324]
  We discuss parallels between Large Language Models (LLMs) and human cognition. We show how generative AI can drive personalized learning at scale.
  arXiv  Detail & Related papers  (2025-01-12T01:43:39Z)
• Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective [77.94874338927492]
  OpenAI has claimed that the main techinique behinds o1 is the reinforcement learning. This paper analyzes the roadmap to achieving o1 from the perspective of reinforcement learning.
  arXiv  Detail & Related papers  (2024-12-18T18:24:47Z)
• SPIRE: Synergistic Planning, Imitation, and Reinforcement Learning for Long-Horizon Manipulation [58.14969377419633]
  We propose spire, a system that decomposes tasks into smaller learning subproblems and second combines imitation and reinforcement learning to maximize their strengths. We find that spire outperforms prior approaches that integrate imitation learning, reinforcement learning, and planning by 35% to 50% in average task performance.
  arXiv  Detail & Related papers  (2024-10-23T17:42:07Z)
• Generative AI in Education: A Study of Educators' Awareness, Sentiments, and Influencing Factors [2.217351976766501]
  This study delves into university instructors' experiences and attitudes toward AI language models. We find no correlation between teaching style and attitude toward generative AI. While CS educators show far more confidence in their technical understanding of generative AI tools, they show no more confidence in their ability to detect AI-generated work.
  arXiv  Detail & Related papers  (2024-03-22T19:21:29Z)
• Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning [50.47568731994238]
  Key method for creating Artificial Intelligence (AI) agents is Reinforcement Learning (RL) This paper presents a general framework model for integrating and learning structured reasoning into AI agents' policies.
  arXiv  Detail & Related papers  (2023-12-22T17:57:57Z)
• Artificial Intelligence-Enabled Intelligent Assistant for Personalized and Adaptive Learning in Higher Education [0.2812395851874055]
  This paper presents a novel framework, Artificial Intelligence-Enabled Intelligent Assistant (AIIA) for personalized and adaptive learning in higher education. The AIIA system leverages advanced AI and Natural Language Processing (NLP) techniques to create an interactive and engaging learning platform.
  arXiv  Detail & Related papers  (2023-09-19T19:31:15Z)
• Incremental procedural and sensorimotor learning in cognitive humanoid robots [52.77024349608834]
  This work presents a cognitive agent that can learn procedures incrementally. We show the cognitive functions required in each substage and how adding new functions helps address tasks previously unsolved by the agent. Results show that this approach is capable of solving complex tasks incrementally.
  arXiv  Detail & Related papers  (2023-04-30T22:51:31Z)
• Teachable Reinforcement Learning via Advice Distillation [161.43457947665073]
  We propose a new supervision paradigm for interactive learning based on "teachable" decision-making systems that learn from structured advice provided by an external teacher. We show that agents that learn from advice can acquire new skills with significantly less human supervision than standard reinforcement learning algorithms.
  arXiv  Detail & Related papers  (2022-03-19T03:22:57Z)
• AI Autonomy : Self-Initiated Open-World Continual Learning and Adaptation [16.96197233523911]
  This paper proposes a framework for the research of building autonomous and continual learning enabled AI agents. The key challenge is how to automate the process so that it is carried out continually on the agent's own initiative.
  arXiv  Detail & Related papers  (2022-03-17T00:07:02Z)
• Toddler-Guidance Learning: Impacts of Critical Period on Multimodal AI Agents [18.610737380842494]
  We adapt the notion of critical periods to learning in AI agents and investigate the critical period in the virtual environment for AI agents. We build up a toddler-like environment with VECA toolkit to mimic human toddlers' learning characteristics. We evaluate the impact of critical periods on AI agents from two perspectives: how and when they are guided best in both uni- and multimodal learning.
  arXiv  Detail & Related papers  (2022-01-12T10:57:40Z)
• Self-Initiated Open World Learning for Autonomous AI Agents [16.41396764793912]
  As more and more AI agents are used in practice, it is time to think about how to make these agents fully autonomous. This paper proposes a theoretic framework for this learning paradigm to promote the research of building Self-initiated Open world Learning agents.
  arXiv  Detail & Related papers  (2021-10-21T18:11:02Z)
• PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training [94.87393610927812]
  We present an off-policy, interactive reinforcement learning algorithm that capitalizes on the strengths of both feedback and off-policy learning. We demonstrate that our approach is capable of learning tasks of higher complexity than previously considered by human-in-the-loop methods.
  arXiv  Detail & Related papers  (2021-06-09T14:10:50Z)
• Explainable Active Learning (XAL): An Empirical Study of How Local Explanations Impact Annotator Experience [76.9910678786031]
  We propose a novel paradigm of explainable active learning (XAL), by introducing techniques from the recently surging field of explainable AI (XAI) into an Active Learning setting. Our study shows benefits of AI explanation as interfaces for machine teaching--supporting trust calibration and enabling rich forms of teaching feedback, and potential drawbacks--anchoring effect with the model judgment and cognitive workload.
  arXiv  Detail & Related papers  (2020-01-24T22:52:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.