By Amira Abdel-Aziz, Weiwei Cheng (auth.), Sarah Jane Delany, Santiago Ontañón (eds.)
This publication constitutes the refereed complaints of the twenty first overseas convention on Case-Based Reasoning learn and improvement (ICCBR 2013) held in Saratoga Springs, long island, united states, in July 2013.
The 17 revised complete papers offered including nine posters were
carefully reviewed and chosen from 39 submissions. The shows and posters lined quite a lot of CBR issues of curiosity either to researchers and practitioners together with case retrieval and version, similarity overview, case base upkeep, wisdom administration, recommender structures, multiagent structures, textual CBR, and purposes to healthcare and desktop games.
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Extra resources for Case-Based Reasoning Research and Development: 21st International Conference, ICCBR 2013, Saratoga Springs, NY, USA, July 8-11, 2013. Proceedings
For example, rangers are worth 70 points and ballistas are worth 100. So if both teams score 1000, it means they each killed 1000 points worth of units on the opposing team, resulting in a tie. 05 10 15 20 25 30 Case Base Size 35 40 Fig. 4. 00 1 2 3 4 Goal 5 6 7 Fig. 5. Goal Selection Distribution Results The ﬁrst graph, Figure 4, shows the accuracy of the case-based system against the evidence-based system. We ran the evidence scoring system on each of the 48 scenarios, and recorded the goals chosen.
Fig. 3 shows how the veriﬁcation event takes place during a conversation. This example was taken from a live system and shows how the user’s repeat task is optimized by the learning system, satisfying our primary goal of reducing the number of steps for repetitive tasks. Note that the initial conversation will need to be repeated as many times as this speciﬁc user has deﬁned in their preferences before a rule will be constructed. Initial Conversation(s) Veriﬁcation of New Rule User: text Fred NLS: I found more than one phone number for ’Fred’.
M. ) ICCBR 2007. LNCS (LNAI), vol. 4626, pp. 164–178. Springer, Heidelberg (2007) 8. : Automated Planning: Theory and Practice. Morgan Kaufmann (2004) 9. : Using Meta-reasoning to Improve the Performance of Case-Based Planning. C. ) ICCBR 2009. LNCS, vol. 5650, pp. 210–224. Springer, Heidelberg (2009) 10. : Goal-driven autonomy in a Navy strategy simulation. In: Proceedings of the Twenty-Fourth AAAI Conference on Artiﬁcial Intelligence. AAAI Press, Atlanta (2010) 11. : Perpetual self-aware cognitive agents.