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000914767 020__ $$a9783030303914$$q(electronic book)
000914767 020__ $$a3030303918$$q(electronic book)
000914767 020__ $$z9783030303907
000914767 0247_ $$a10.1007/978-3-030-30391-4$$2doi
000914767 035__ $$aSP(OCoLC)on1120754443
000914767 035__ $$aSP(OCoLC)1120754443
000914767 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dUKMGB$$dOCLCF$$dEBLCP
000914767 049__ $$aISEA
000914767 050_4 $$aQA76.76.I58
000914767 08204 $$a006.3/0285436$$223
000914767 1112_ $$aEXTRAAMAS (Workshop)$$n(1st :$$d2019 :$$cMontréal, Québec)
000914767 24510 $$aExplainable, transparent autonomous agents and multi-agent systems :$$bfirst International Workshop, EXTRAAMAS 2019, Montreal, QC, Canada, May 13-14, 2019, Revised selected papers /$$cDavide Calvaresi, Amro Najjar, Michael Schumacher, Kary Främling (eds.).
000914767 2463_ $$aEXTRAAMAS 2019
000914767 264_1 $$aCham, Switzerland :$$bSpringer,$$c2019.
000914767 300__ $$a1 online resource (x, 221 pages) :$$billustrations.
000914767 336__ $$atext$$btxt$$2rdacontent
000914767 337__ $$acomputer$$bc$$2rdamedia
000914767 338__ $$aonline resource$$bcr$$2rdacarrier
000914767 4901_ $$aLecture notes in artificial intelligence
000914767 4901_ $$aLecture notes in computer science ;$$v11763
000914767 4901_ $$aLNCS sublibrary. SL 7, Artificial intelligence
000914767 500__ $$aIncludes author index.
000914767 5050_ $$aIntro; Preface; Organization; Contents; Explanation and Transparency; Towards a Transparent Deep Ensemble Method Based on Multiagent Argumentation; 1 Introduction; 2 State of Art; 2.1 Ensemble Method; 2.2 Argumentation in ML; 2.3 Explainable Intelligent Systems; 3 Method; 3.1 Arguments Extraction Phase; 3.2 Multiagent Argumentation Phase; 4 Case Study; 4.1 Data Description; 4.2 Scenarios Illustration; 5 Experimentation; 6 Conclusion; References; Effects of Agents' Transparency on Teamwork; 1 Introduction; 2 Related Work; 3 Research Design and Methods; 4 Objective and Hypothesis
000914767 5058_ $$a4.1 Materials and Methods4.2 Metrics and Data Collection; 4.3 Procedure; 5 User Study; 5.1 Participants; 5.2 Data Analysis; 6 Discussion; 7 Conclusions; References; Explainable Robots; Explainable Multi-Agent Systems Through Blockchain Technology; 1 Introduction; 2 Background; 2.1 Trust; 2.2 Explainability; 2.3 Blockchain Technology; 3 Application Domains; 4 Challenges; 4.1 RC Scenarios; 4.2 LT Scenarios; 5 Proposed Solution; 5.1 RC; 5.2 LT; 6 Explainability and BCT: The UAVs Package Delivery Use Case; 7 Discussion; 8 Conclusions; References; Explaining Sympathetic Actions of Rational Agents
000914767 5058_ $$a1 Introduction2 Background; 2.1 Behavioral Economics and Multi-agent Systems; 2.2 Machine Theory of Mind; 2.3 Explainable Artificial Intelligence (XAI) and Explainable Agents; 3 A Taxonomy of Goals for Sympathetic Actions; 4 Ways to Explain Sympathetic Actions; 5 Towards an Empirical Assessment; 5.1 Study Design; 5.2 Data Collection and Analysis; 5.3 Results; 5.4 Interpretation; 6 Discussion; 6.1 Sympathetic Actions of Learning Agents; 6.2 Limitations; 6.3 Future Work; 7 Conclusion; References; Conversational Interfaces for Explainable AI: A Human-Centred Approach; 1 Introduction
000914767 5058_ $$a2 Designing a Wizard of Oz Experiment3 User Behaviour in Conversational Interfaces for XAI; 3.1 Users' Question Formulation; 3.2 Strategies of Interaction; 4 Implications for the Implementation of CIs; 4.1 The Information Privacy Trade-Off; 4.2 The Necessity of Repair Questions; 4.3 Question Intents for Better Machine Understanding; 5 Related Work; 6 Discussion; 7 Conclusion and Outlook; References; Opening the Black Box; Explanations of Black-Box Model Predictions by Contextual Importance and Utility; Abstract; 1 Introduction; 2 Background; 3 State of the Art
000914767 5058_ $$a4 Contextual Importance and Contextual Utility5 Examples of CI and CU Method to Extract Explanation for Linear and Non-linear Models; 5.1 Visual Explanations for Car Selection Using CI and CU Method; 5.2 Explaining Iris Flower Classification Using CI and CU Method; 6 Discussion; 7 Conclusion; Acknowledgment; References; Explainable Artificial Intelligence Based Heat Recycler Fault Detection in Air Handling Unit; 1 Introduction; 2 Previous Work; 3 Theoretical Background; 3.1 Heat Recycler Unit; 3.2 Support Vector Machine; 3.3 Neural Networks; 3.4 Explainable Artificial Intelligence
000914767 506__ $$aAccess limited to authorized users.
000914767 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed September 24, 2019).
000914767 650_0 $$aIntelligent agents (Computer software)$$vCongresses.
000914767 7001_ $$aCalvaresi, Davide,$$eeditor.
000914767 7001_ $$aNajjar, Amro,$$eeditor.
000914767 7001_ $$aSchumacher, Michael,$$eeditor.
000914767 7001_ $$aFrämling, Kary,$$eeditor.
000914767 830_0 $$aLecture notes in computer science.$$pLecture notes in artificial intelligence.
000914767 830_0 $$aLecture notes in computer science ;$$v11763.
000914767 830_0 $$aLNCS sublibrary.$$nSL 7,$$pArtificial intelligence.
000914767 852__ $$bebk
000914767 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-030-30391-4$$zOnline Access$$91397441.1
000914767 909CO $$ooai:library.usi.edu:914767$$pGLOBAL_SET
000914767 980__ $$aEBOOK
000914767 980__ $$aBIB
000914767 982__ $$aEbook
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