List of papers for presentation

CS785 Intelligent Computer Interfaces

Spring 2016

List of papers for presentation: second half of the course

Multiagent Systems Part I
1. K. Wray and B. Thompson; A distributed communication architectue for dynamic multiagent systems;
  Proceedings of AAAI 2014 workshop on multiagent interaction; 2014
  http://mipc.inf.ed.ac.uk/2014/#papers
2. J. Sleight and E. Durfee; Selectively Injecting Organizational Influences into
  Decision-Making Agents;
  Proceedings of COIN workshop at AAMAS 2012
  http://ict1.tbm.tudelft.nl/coin2012/papers/20120181.pdf
3. E. Jensen et al.; Communication-restricted exploration for robot teams;
  Proceedings of AAAI 2014 workshop on multiagent interaction; 2014
  http://mipc.inf.ed.ac.uk/2014/#p apers
4. J. Hao et al.; Reinforcement social learning of coordination in networked cooperative multiagent systems;
  Proceedings of AAAI 2014 workshop Multiagent interaction; 2014
  http://www.aaai.org/ocs/index.php/WS/AAAIW14/paper/view/8708
5. N. Agmon et al.; Modeling uncertainty in leading ad hoc teams;
  Proceedings of AAMAS 2014 workshop on Multiagent interaction; 2014
  http:///dl.acm.org/citation.cfm?id=2615797
6. S. Liemhetcharat and M. Veloso; Modeling and learning synergy for team formation with heterogenous agents;
  Proceedings of AAMAS 2012; 2012
  http:///dl.acm.org/citation.cfm?id=2343628
7. K. Bogert and P. Doshi; Toward Estimating Others' Transition Models Under Occlusion for Multi-Robot IRL;
  Proceedings of IJCAI 2015; 2015
  http://www.aaai.org/ocs/index.php/IJCAI/IJCAI15/paper/view/11445
8. R. Brafman.; A privacy preserving algorithm for multi agent planning and search;
  Proceedings of IJCAI 2015; 2015
  http://ijcai.org/Proceedings/15/Papers/219.pdf
9. M. Baldoni et al.; Composing and verifying committent-based multiagent protocols;
  Proceedings of ICJAI 2015; 2015
  http:///dl.acm.org/citation.cfm?id=2832251
10. L. Marcolino et al.; Give a hard problem to a diverse team: exploring large action spaces;
  Proceedings of AAAI 2014; 2014
  http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/viewFile/8257/8598
Multiagent Systems Part II (auctions and e-commerce)
1. J. Oren et al; Efficient vote elicitation under candidate uncertainty;
  Proceedings of IJCAI 2013; 2013
  http:///dl.acm.org/citation.cfm?id=2540174
2. N. Nguyen et al.; Strategy-Proofness of scoring allocation correspondence for indivisible goods;
  Proceedings of IJCAI 2015; 2015
  http://ijcai.org/Proceedings/15/Papers/163.pdf
3. Z. Wang and P. Tang; Optimal mechanism design for partially rational bidders;
  Proceedings of IJCAI 2015; 2015
  http://ijcai.org/Proceedings/15/Papers/024.pdf
4. A. Bolcan et al.; Learning cooperative games;
  Proceedings of IJCAI 2015; 2015
  http://ijcai.org/Proceedings/15/Papers/073.pdf
5. H. Xu et al.; Security games with information leakage: modeling and computation;
  Proceedings of IJCAI 2015; 2015
  http://www.aaai.org/ocs/index.php/IJCAI/IJCAI15/paper/view/11351
6. Y. Luo and P. Tang; Mechanism design and implementation for lung exchange;
  Proceedings of IJCAI 2015; 2015
  http://www.aaai.org/ocs/index.php/IJCAI/IJCAI15/paper/view/10748
7. C. Wang et al.; Selling reserved instances in cloud computing;
  Proceedings of IJCAI 2015; 2015
  http://ijcai.org/Proceedings/15/Papers/038.pdf
8. S. Branzei et al.; An algorithmic framework for strategic fair division;
  Proceedings of AAAI 2016
  http://cs.au.dk/~simina
9. C. Kroer and T. Sandholm; Limited lookahead in incomplete information games;
  Proceedings of IJCAI 2015; 2015
  https://www.cs.cmu.edu/~sandholm/limited-look-ahead.ijcai15.pdf
10. M. Brill and V. Conitzer; Strategic voting and strategic candidacy;
  Proceedings of AAAI 2015; 2015
  http://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/viewFile/9968/9374
Data Mining
1. Z. Lu et al; Social image parsing by cross-model data refinement;
  Proceedings of IJCAI 2015; 2015
  http://ijcai.org/Proceedings/15/Papers/307.pdf
2. M. Liu et al.; A clustering algorithm for massive amount of texts;
  Proceedings of IJCAI 2015; 2015
  http://ijcai.org/Proceedings/15/Papers/333.pdf
3. C. Wong and L. Cao; Medical synonym extraction with concept space models;
  Proceedings of IJCAI 2015; 2015
  http://arxiv.org/pdf/1506.00528
4. Q. Liu et al.; Automated rule selection for aspect extraction in opinion mining;
  Proceedings of IJCAI 2015; 2015
  http://ijcai.org/Proceedings/15/Papers/186.pdf
5. M. Khan et al.; Exploring personalized command recommendations based on information found in web documentation;
  Proceedings of IUI 2015; 2015
  www.cs.umanitoba.ca/~bunt/papers/2015_IUI_Khan.pdf
6. A. Costa et al.; RSC: Mining and modeling temporal activity in social media;
  Proceedings of KDD 2015; 2015
  http:///dl.acm.org/citation.cfm?id=2783294
Intelligent Information Retrieval
1. A. Singla et al.; Information gathering in networks via active exploration;
  Proceedings of IJCAI 2015; 2015
  http://ijcai.org/Proceedings/15/Papers/143.pdf
2. P. Sczeparski et al.; The game-theoretic interaction index on social networks with application to link prediction and community detection;
  Proceedings of IJCAI 2015; 2015
  http://ijcai.org/Proceedings/15/Papers/096.pdf
3. C. Yang et al.; Network representation learning with rich text information;
  Proceedings of IJCAI 2015; 2015
  http://nlp.csai.tsinghua.edu.cn/~lzy/publications/ijcai2015_network.pdf
4. J. Liu et al.; Representing documents via latent keyphrase inference;
  Proceedings of WWW 2016; 2016
  http:///dl.acm.org/citation.cfm?id=2883088
5. Q. Liu et al.; Web page classification based on uncorrelated semi-supervised intra-view and inter-voew manifold discriminant feature extraction;
  Proceedings of IJCAI 2015; 2015
  http://ijcai.org/Proceedings/15/Papers/319.pdf
6. C. Li and Y. Liu; Joint POS tagging and text normalization for informal text;
  Proceedings of IJCAI 2015; 2015
  http://ijcai.org/Proceedings/15/Papers/182.pdf
7. D. Palguna et al.; Analysis of sampling algorithms for Twitter;
  Proceedings of IJCAI 2015; 2015
  http://www.aaai.org/ocs/index.php/IJCAI/IJCAI15/paper/view/10690
8. A. Schultz et al.; Small-scale incident detection based on microposts;
  Proceedings of Hypertext 2015; 2015
  http:///dl.acm.org/citation.cfm?id=2791038
Case-Based Reasoning and Knowledge Bases
1. Q. Wang and B. Wang; Knowledge base completion using embeddings and rules;
  Proceedings of IJCAI 2015; 2015
  http://ijcai.org/Proceedings/15/Papers/264.pdf
2. M. Samadai et al.; AskWorld: Budget-sensitive query evaluation for knowledge-on-demand;
  Proceedings of IJCAI 2015; 2015
  talukdar.net//papers/ijcai15_AskWorld.pdf
3. D. Miryenka et al.; Bootstrapping domain ontologies from Wikipedia: a uniform approach;
  Proceedings of IJCAI 2015; 2015
  http://ijcai.org/Proceedings/15/Papers/210.pdf
4. M. Alam et al.; Mining definitions from RDF annotations using formal concept analysis;
  Procedings of IJCAI 2015; 2015
  http://ijcai.org/Proceedings/15/Papers/121.pdf
5. R. Platon et al.; CBR model for predicting a building's electricity use: on-line implementation in the absence of historical data;
  Proceedings of ICCBR 2015; 2015
  link.springer.com/chapter/10.1007/978-3-319-24586-7_21
6. S. Vattam and D. Aha; Case-based plan recognition under imperfect observability;
  Proceedings of ICCBR 2015; 2015
  link.springer.com/chapter/10.1007/978-3-319-24586-7_26
7. A. Abdel-Aziz and E. Huellermeier; Case base maintenance in preference-based CBR;
  Proceedings of ICCBR 2015; 2015
  link.springer.com/chapter/10.1007/978-3-319-24586-7_1
8. V. Jalali and D. Leake; CBR meets big data: a case study of large-scale adaptation rule generation;
  Proceedings of ICCBR 2015; 2015
  www.cs.indiana.edu/ftp/leake/p-15-03.pdf
Intelligent Tutoring
1. D. Joyner and A. Goel; Improving inquiry-driven modeling in science education through interaction with intelligent tutoring agents;
  Proceedings of IUI 2015; 2015
  http:///dl.acm.org/citation.cfm?id=2701398
2. A. Carbonara et al.; Incentivizing peer grading in MOOCs: an audit game approach;
  Proceedings of IJCAI 2015; 2015
  www.cs.cmu.edu/~yairzick/papers/auditing_moocs.pdf
3. A. Smith et al.; Diagrammatic student models: modeling student drawing performance with deep learning;
  Proceedings of UMAP 2015; 2015
  link.springer.com/chapter/10.1007/978-3-319-20267-9_18
4. M. Streeter; Mixture modeling of individual learning curves;
  Proceedings of Educational Data Mining (EDM) 2015; 2015
  http://www.cs.cmu.edu/~matts
5. Y. Jiang et al.; Comparing novice and experienced students within virtual performance assessments;
  Proceedings of Educational Data Mining (EDM) 2015; 2015
  http://www.columbia.edu/~rsb2162/publications.html
6. A. Vail et al.; The Mars and Venus effect: the influence of user gender on the effectiveness of adaptive task support;
  Proceedings of UMAP 2015; 2015
  link.springer.com/chapter/10.1007/978-3-319-20267-9_22

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