Meta-Reasoning: What Can We Learn from Meta-Memory?
نویسندگان
چکیده
منابع مشابه
Using Meta-reasoning to Improve the Performance of Case-Based Planning
Case-based planning (CBP) systems are based on the idea of reusing past successful plans for solving new problems. Previous research has shown the ability of meta-reasoning approaches to improve the performance of CBP systems. In this paper we present a new meta-reasoning approach for autonomously improving the performance of CBP systems that operate in real-time domains. Our approach uses fail...
متن کاملAdopting New International Health Instruments – What Can We Learn From the FCTC?; Comment on “The Legal Strength of International Health Instruments - What It Brings to Global Health Governance?”
This Commentary forms a response to Nikogosian’s and Kickbusch’s forward-looking perspective about the legal strength of international health instruments. Building on their arguments, in this commentary we consider what we can learn from the Framework Convention on Tobacco Control (FCTC) for the adoption of new legal international health instruments.
متن کاملGoal-Driven Learning in the GILA Integrated Intelligence Architecture
Goal Driven Learning (GDL) focuses on systems that determine by themselves what has to be learnt and how to learn it. Typically GDL systems use meta-reasoning capabilities over a base reasoner, identifying learning goals and devising strategies. In this paper we present a novel GDL technique to deal with complex AI systems where the metareasoning module has to analyze the reasoning trace of mul...
متن کاملMeta Level Reasoning and Default Reasoning
In this paper, we propose a logic framework for meta level reasoning as well as default reasoning in a general sense, based on an arbitrary underlying logic. In this framework, meta level reasoning is the task of how to deduce new meta level rules by giving a set of rules, whilst default reasoning is the problem of what are the possible candidate beliefs by giving them. We define the semantics ...
متن کاملCombining Model-Based Meta-Reasoning and Reinforcement Learning for Adapting Game-Playing Agents
Human experience with interactive games will be enhanced if the game-playing software agents learn from their failures and do not make the same mistakes over and over again. Reinforcement learning, e.g., Q-Learning, provides one method for learning from failures. Model-based meta-reasoning that uses an agent’s self-model for blame assignment provides another. In this paper, we combine the two m...
متن کامل