Case-based Reasoning for Experience-based Collaborative Risk Management

In a collaborative risk management scenario, project stakeholders often need natural forms of recording and reusing past risk management experiences so that they could better assess whether there are threats to the goals of new projects. The contribution of this paper is to propose an enhanced case-based reasoning (CBR) approach to support project participants to exploit such experiences which are here expressed as collaborative risk management discussion cases. The paper shows how these debates are structured through the exploitation of a dialogue game protocol for risk management. Then, it discusses how users can utilize queries based on facts and arguments so that past risk discussion cases could be retrieved from a case base. Attention is also given to case-based explanation templates, which are relevant for the understanding of key moves of argumentation in debate trees recorded in such enhanced cases retrieved. To demonstrate the practical utility of this approach, a case study involving the collaborative experience-based risk management of a software project is discussed. Keywords-component: Case-Based Reasoning; Collaborative Risk Management Tool; Dialogue Game; Argumentation.

[1]  Luís A. Lima Silva,et al.  A Dialogue Game Approach to Collaborative Risk Management (S) , 2013, SEKE.

[2]  Kevin D. Ashley,et al.  Law, learning and representation , 2003, Artif. Intell..

[3]  W. Duncan A GUIDE TO THE PROJECT MANAGEMENT BODY OF KNOWLEDGE , 1996 .

[4]  M. P. Gupta,et al.  Assessment of risk in e-governance projects: an application of product moment correlation and cluster analysis techniques , 2011, Electron. Gov. an Int. J..

[5]  Philippe Kruchten,et al.  Contextualizing agile software development , 2013, J. Softw. Evol. Process..

[6]  Peter H. A. Sneath,et al.  Numerical Taxonomy: The Principles and Practice of Numerical Classification , 1973 .

[7]  Bernard Moulin,et al.  Explanation and Argumentation Capabilities:Towards the Creation of More Persuasive Agents , 2002, Artificial Intelligence Review.

[8]  Padraig Cunningham,et al.  An Evaluation of the Usefulness of Case-Based Explanation , 2003, ICCBR.

[9]  Yong Hu,et al.  Software project risk analysis using Bayesian networks with causality constraints , 2013, Decis. Support Syst..

[10]  Agnar Aamodt,et al.  Explanation in Case-Based Reasoning–Perspectives and Goals , 2005, Artificial Intelligence Review.

[11]  Peter McBurney,et al.  Dialogue Games for Agent Argumentation , 2009, Argumentation in Artificial Intelligence.

[12]  Barry W. Boehm,et al.  Using Risk to Balance Agile and Plan-Driven Methods , 2003, Computer.

[13]  Trevor J. M. Bench-Capon,et al.  Argumentation in artificial intelligence , 2007, Artif. Intell..

[14]  Barry Smyth,et al.  Retrieval, reuse, revision and retention in case-based reasoning , 2005, The Knowledge Engineering Review.

[15]  Vicent J. Botti,et al.  Argue to agree: A case-based argumentation approach , 2013, Int. J. Approx. Reason..

[16]  William C. Regli,et al.  A Survey of Design Rationale Systems: Approaches, Representation, Capture and Retrieval , 2000, Engineering with Computers.

[17]  Michael Coles,et al.  Full-Text Search , 2015 .

[18]  阿里山國家風景區入口網 Full text search , 2011 .