Examining the Modelling Capabilities of Defeasible Argumentation and non-Monotonic Fuzzy Reasoning
Autor: | Lucas Rizzo, Pierpaolo Dondio, Luca Longo |
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Rok vydání: | 2021 |
Předmět: |
Computer and Systems Architecture
Information Systems and Management Knowledge representation and reasoning Computer science Empirical research Inference Monotonic function 02 engineering and technology Defeasible reasoning Fuzzy logic Management Information Systems Argumentation theory Artificial Intelligence Argument 020204 information systems Argumentation 0202 electrical engineering electronic engineering information engineering Computer Engineering business.industry Semantic reasoner Mental workload Non-monotonic reasoning Knowledge-representation 020201 artificial intelligence & image processing Artificial intelligence business Software |
Zdroj: | Articles |
Popis: | Knowledge-representation and reasoning methods have been extensively researched within Artificial Intelligence. Among these, argumentation has emerged as an ideal paradigm for inference under uncertainty with conflicting knowledge. Its value has been predominantly demonstrated via analyses of the topological structure of graphs of arguments and its formal properties. However, limited research exists on the examination and comparison of its inferential capacity in real-world modelling tasks and against other knowledge-representation and non-monotonic reasoning methods. This study is focused on a novel comparison between defeasible argumentation and non-monotonic fuzzy reasoning when applied to the representation of the ill-defined construct of human mental workload and its assessment. Different argument-based and non-monotonic fuzzy reasoning models have been designed considering knowledge-bases of incremental complexity containing uncertain and conflicting information provided by a human reasoner. Findings showed how their inferences have a moderate convergent and face validity when compared respectively to those of an existing baseline instrument for mental workload assessment, and to a perception of mental workload self-reported by human participants. This confirmed how these models also reasonably represent the construct under consideration. Furthermore, argument-based models had on average a lower mean squared error against the self-reported perception of mental workload when compared to fuzzy-reasoning models and the baseline instrument. The contribution of this research is to provide scholars, interested in formalisms on knowledge-representation and non-monotonic reasoning, with a novel approach for empirically comparing their inferential capacity. |
Databáze: | OpenAIRE |
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