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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 ...
CIALDEA, Marta, Marta Cialdea
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, 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.
COSTANTINI, STEFANIA
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Meta-reasoning in Assembly Robots
2021As robots become increasingly pervasive in human society, there is a need for developing theoretical frameworks for “human–machine shared contexts.” In this chapter, we develop a framework for endowing robots with a human-like capacity for meta-reasoning. We consider the case of an assembly robot that is given a task slightly different from the one for
Priyam Parashar, Ashok K. Goel
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Meta-reasoning: An incremental compilation approach
[1991] Proceedings. Seventh International Conference on Data Engineering, 2002An incremental compilation approach to meta-reasoning is presented together with a method to update dynamically changing knowledge bases. The compilation process translates meta-level specification of facts and hypotheses into sentences of clausal logic.
Abdul Sattar 0001, Randy Goebel
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2005
The ACL2 system is based upon a first-order logic and implements traditional first-order reasoning techniques, notably (conditional) rewriting, as well as extensions including mathematical induction and a “functional instantiation” capability for mimicking second-order reasoning.
Warren A. Hunt Jr. +4 more
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The ACL2 system is based upon a first-order logic and implements traditional first-order reasoning techniques, notably (conditional) rewriting, as well as extensions including mathematical induction and a “functional instantiation” capability for mimicking second-order reasoning.
Warren A. Hunt Jr. +4 more
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A Case Study in Computer-Assisted Meta-reasoning
2021We 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 +2 more
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Meta-reasoning for a distributed agent architecture
Proceedings of the 33rd Southeastern Symposium on System Theory (Cat. No.01EX460), 2002Agent based computing offers the ability to decentralize computing solutions by incorporating autonomy and intelligence into cooperating, distributed applications. It provides an effective medium for expressing solutions to problems that involve interaction with real-world environments and allows modelling of the world state and its dynamics.
D. Chelberg +4 more
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Meta‐Reasoning and Practical Deliberation
Philosophy and Phenomenological Research, 2009Fallibilism about our practical judgments is uncontroversial: we are frequently wrong when we make decisions about what to do often badly wrong and everyone knows it. But efforts to characterize and respond to our lamentably imperfect decision-making have lagged. There has been some work in related fields.
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Meta-reasoning Methods for Agent’s Intention Modelling
2005Intention 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 Pechoucek +2 more
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A Decision Theoretic Meta-reasoner for Constraint Optimization
2005Solving constraint optimization problems is hard because it is not enough to find the best solution; an algorithm does not know a candidate is the best solution until it has proven that there are no better solutions The proof can be long, compared to the time spent to find a good solution In the cases where there are resource bounds, the proof of ...
Jingfang Zheng, Michael C. Horsch
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