Results 231 to 240 of about 47,988 (269)

Meta-Reasoning and Student Modelling

1992
The student modelling process in Intelligent Educational Systems is the process of inferring a model of the student’s knowledge by analysing his or her behaviour. When the student’s behaviour is the result of a reasoning activity, the task of building a student model involves the system’s reasoning about another agent’s knowledge and reasoning (meta ...
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Meta-reasoning: A Survey

2002
We present the basic principles and possible applications of systems capable of meta-reasoning and reflection. After a discussion of the seminal approaches, we outline our own perception of the state of the art, mainly but not only in computational logic and logic programming.
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Transferable strategic meta-reasoning models

2013
How do strategic agents make decisions? For the first time, a confluence of advances in agent design, formation of massive online data sets of social behavior, and computational techniques have allowed for researchers to construct and learn much richer models than before.
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A Case Study in Computer-Assisted Meta-reasoning

2021
We discuss human and mechanized reasoning with regards to the use of proof assistants, in particular Isabelle/HOL. We use the development of novel NAND- and NOR-based micro provers as a case study. Current, widely available automated reasoning technology is suitable for assisting humans with certain types of reasoning, like finding proofs for well ...
Asta Halkjær From, Simon Tobias Lund, Jørgen Villadsen   +2 more
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Meta-reasoning Methods for Agent’s Intention Modelling

2005
Intention modelling in self-interested and adversarial communities of agents is a challenging issue. This contribution discusses the role of modelling and meta-reasoning in intention modelling. The formal model of deductive and inductive meta-reasoning is presented and supported by experimental implementations.
Michal Pěchouček, Jan Tožička, Vladimír Mařík   +2 more
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Meta-reasoning for Agents’ Private Knowledge Detection

2003
Agent’s meta-reasoning is a computational process that implements agent’s capability to reason on a higher level about another agent or a community of agents. There is a potential for meta-reasoning in multi-agent systems. Meta-reasoning can be used for reconstructing agents’ private knowledge, their mental states and for prediction of their future ...
Jan Tožička, Jaroslav Bárta, Michal Pěchouček   +2 more
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Meta-Reasoning about Decisions in Autonomous Semi-Intelligent Systems

Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence, 2020
For intelligent systems to become autonomous in any real sense, they need an ability to make decisions on situations that were not entirely conceived of at compile-time. Machine learning algorithms are excellent in mimicking the behaviour of some gold standard role model, and this can include decision making by the role model.
Danielson, M., Ekenberg, L.
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Meta-Reasoning for Data Analysis Tool Allocation

1997
It is desirable that data analysis tools become more autonomous in managing their computational resources, minimizing risk and cost, assessing their errors, developing new representations, and integrating with other data analysis tools. To aid in this development we introduce a design for using Meta-Reasoning in a Data Analysis Tool Allocation system ...
Robert Levinson, Jeff Wilkinson
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Machine Learning, Meta-Reasoning and Logics

1990
I. Meta-Reasoning and Machine Learning.- A Metalevel Manifesto.- A Sketch of Autonomous Learning using Declarative Bias.- Shift of Bias as Non-Monotonic Reasoning.- Mutual Constraints on Representation and Inference.- Meta-Reasoning: Transcription of Invited Lecture by Luigia Aiello.- Discussion.- II.
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Visualization of Meta-Reasoning in Multi-Agent Systems

2005
This paper describes the advances of our research on visualization of multi-agent systems (MAS) for purposes of analysis, monitoring and debugging. MAS are getting more complex and widely used, such analysis tools are highly beneficial in order to achieve better understanding of agents’ behaviour.
D. Řehoř, J. Tožička, P. Slavík
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