Explainable, Transparent Autonomous Agents and Multi-Agent Systems

First International Workshop, EXTRAAMAS 2019, Montreal, QC, Canada, May 13–14, 2019, Revised Selected Papers

AvDavide Calvaresi,Amro Najjar

Häftad, Engelska, 2019

Del 11763 i serien Lecture Notes in Computer Science

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Beskrivning

This book constitutes the proceedings of the First International Workshop on Explainable, Transparent Autonomous Agents and Multi-Agent Systems, EXTRAAMAS 2019, held in Montreal, Canada, in May 2019.The 12 revised and extended papers presented were carefully selected from 23 submissions. They are organized in topical sections on explanation and transparency; explainable robots; opening the black box; explainable agent simulations; planning and argumentation; explainable AI and cognitive science.

Produktinformation

Utgivningsdatum:2019-09-11
Mått:155 x 235 x 13 mm
Vikt:359 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:221
Förlag:Springer Nature Switzerland AG
ISBN:9783030303907

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Innehållsförteckning

Explanation and Transparency.- Towards a transparent deep ensemble method based on multiagent Argumentation.- Effects of Agents' Transparency on Teamwork.- Explainable Robots.- Explainable Multi-Agent Systems through Blockchain Technology Authors.- Explaining Sympathetic Actions of Rational Agents.- Conversational Interfaces for Explainable AI: A Human-Centered Approach.- Opening the Black Box.- Explanations of Black-Box Model Predictions by Contextual Importance and Utility.- Explainable Artificial Intelligence based Heat Recycler Fault Detection in Air Handling Unit.- Explainable Agent Simulations.- Explaining Aggregate Behaviour in Cognitive Agent Simulations using Explanation.- BEN : An Agent Architecture for Explainable and Expressive Behavior in Social Simulation.- Planning and Argumentation.- Temporal Multiagent Plan Execution: Explaining what Happened.- Explainable Argumentation for Wellness Consultation.- Explainable AI and Cognitive Science.- How Cognitive Science Impacts AI and What We Can Learn From It.