CS785 Intelligent Computer Interfaces

Spring 2016

 

List of papers for presentation: first half of the course
 

  1. Plan Recognition

    1. F. Bisson et al.; Using a recursive neural network to learn an agent's decision model for plan recognition;
      Proceedings of IJCAI 2015; 2015
      http://ijcai.org/Proceedings/15/Papers/134.pdf

    2. D. Smith and H. Lieberman; Using plan recognition for interpreting referring expressions;
      Proceedings of Plan, Activity and Intent Recognition workshop at AAAI 2013; 2013
      http://www.aaai.org/ocs/index.php/WS/AAAIW13/paper/view/7175

    3. N. Benabbouti and P. Perny; Combining Preference Elicitation and Search in Multiobjective State-Space Graphs;
      Proceedings of IJCAI 2015; 2015
      http://ijcai.org/Proceedings/15/Papers/048.pdf

    4. M. van Riemsdijk and N. Yorke-Smith; Towards reasoning with partial goal satisfaction in intelligent agents;
      Proceedings of AAMAS10 workshop on programming multiagent systems
      http://www.aub.edu.lb/~nysmith/papers/n74.pdf

    5. Y. Martin et al.; Fast goal recognition technique based on interaction estimates;
      Proceedings of IJCAI 2015; 2015
      http://ti.arc.nasa.gov/publications/24965/download

    6. S. Keren, A. Gal and E. Karpas; Goal recognition design for non-optimal agents;
      Proceedings of AAAI 2015; 2015
      http://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/view/9439

    7. M. Ramirez and H. Geffner; Goal recognition over POMDPs: inferring the intention of a POMDP agent;
      Proceedings of IJCAI 2011; 2011
      http://icaps11.icaps-conference.org/proceedings/gaprec/ramirez-geffner.pdf

    8. A. Panella and P. Gmytrasiewicz; Nonparametric Bayesian learning of other agents' policies in interactive POMDPs;
      Proceedings of AAAI 2015 workshop on multiagent interaction without prior coordination (MIPC); 2015
      mipc.inf.ed.ac.uk/2015/#papers

  2. Discourse

    1. P. Vittorio and M. Veloso; Handling complex commands for service robot task requests;
      Proceedings of IJCAI 2015; 2015
      http://ijcai.org/Proceedings/15/Papers/170.pdf

    2. F. Bernardinelli et al. Formal analysis of dialogues on infinite argumentation frameworks;
      Proceedings of IJCAI 2015; 2015
      http://ijcai.org/Proceedings/15/Papers/126.pdf

    3. E. Hadoux et al.; Optimization of probabilistic argumentation with Markov decision models;
      Proceedings of IJCAI 2015; 2015
      http://ijcai.org/Proceedings/15/Papers/284.pdf

    4. T. Jesse et al.; Learning to interpret natural language commands through human-robot dialog;
      Proceedings of IJCAI 2015; 2015
      http://www.cs.utexas.edu/~szhang/2015_CONF_IJCAI_Thomason.pdf

    5. E. Hoque and G. Carenini; ConVisIT: interactive topic modeling for exploring asynchronous online conversations;
      Proceedings of IUI 2015; 2015
      http:///dl.acm.org/citation.cfm?id=2701370

    6. Y. Yang et al.; User-directed non-disruptive topic model update for effective exploration of dynamic content;
      Proceedings of IUI 2015; 2015
      http:///dl.acm.org/citation.cfm?id=2701396

    7. F. Morstatter et al.; Text, topics and turkers: a consensus measure for statistical topics;
      Proceedings of Hypertext 2015; 2015
      http:///dl.acm.org/citation.cfm?id=2791028

    8. B. Dunin-Keplicz and A. Strachocka; Tractable inquiry in information-rich environments;
      Proceedings of IJCAI 2015; 2015
      http://ijcai.org/Procee dings/15/Papers/015.pdf

  3. Natural Language Generation

    1. Q. Liu et al; Real-time community question-answering: exploring content recommendation and user notification strategies;
      Proceedings of IUI 2015; 2015
      http:///dl.acm.org/citation.cfm?id=2701392

    2. J. Yao et al; Compressive document summarization via sparse optimization;
      Proceedings of IJCAI 2005; 2015
      http://ijcai.org/Proceedings/15/Papers/198.pdf

    3. P. Li et al.; Reader-aware multi-document summarization via sparse coding;
      Proceedings of IJCAI 2015; 2015
      http://arxiv.org/pdf/1504.07324

    4. M. Raza et al.; Compositional program synthesis from natural language and examples;
      Proceedings of IJCAI 2015; 2015
      http://research.microsoft.com/pubs/241480/cps1.pdf

    5. W. Yin and Y. Pei; Optimizing sentence modeling and selection for document summarization;
      Proceedings of IJCAI 2015; 2015
      http://ijcai.org/Proceedings/15/Papers/199.pdf

    6. D. Parveen and M. Strube; Integrating importance, non-redundancy and coherence graph-based extractive summarization;
      Proceedings of IJCAI 2015; 2015
      http://ijcai.org/Proceedings/15/Papers/187.pdf

    7. C. Li et al.; Using external resources and joint learning for bigram/weighting in RP-based multi-document summarization;
      Proceedings of IJCAI 2015; 2015
      www.aclweb.org/anthology/N15-1079

  4. User Modeling

    1. X. Liu; Modeling users' dynamic preference for personalized recommendation;
      Proceedings of IJCAI 2015; 2015
      http://ijcai.org/Proceedings/15/Papers/254.pdf

    2. E. Elkind.; Structure in dichotomous preferences;
      Proceedings of IJCAI 2015; 2015
      http://ijcai.org/Proceedings/15/Papers/286.pdf

    3. Y. Sun et al.; Collaborative nowcasting for contextual recommendation;
      Proceedings of WWW 2016; 2016
      http:///dl.acm.org/citation.cfm?id=2874812

    4. X. Song et al.; Interest inference via structure-constrained multi-source multi-task learning;
      Proceedings of IJCAI 2015; 2015
      http://www.comp.nus.edu.sg/~xuemeng/ijcai2015_song.pdf

    5. R. Wu et al.; Cognitive modeling for predicting examinee performance;
      Proceedings of IJCAI 2015; 2015
      http://ijcai.org/Proceedings/15/Papers/148.pdf

    6. O. Polozov et al.; Personalized mathematical word problem generation;
      Proceedings of IJCAI 2015; 2015
      http://homes.cs.washington.edu/~polozov/papers/ijcai2015-word-problems.pdf

    7. S. J. Ahn et al.; Personalized search: reconsidering the value of open user models;
      Proceedings of IUI 2015; 2015
      http:///dl.acm.org/citation.cfm?id=2701410

    8. D. Liang et al.; Modeling user exposure in recommendation;
      Proceedings of WWW 2016; 2016
      http:///dl.acm.org/citation.cfm?id=2883090

  5. Intelligent Agents

    1. X. Wang et al; Recommendation algorithms for optimizing hit rate, user satisfaction and website revenue;
      Proceedings of IJCAI 2015; 2015
      http://ijcai.org/Proceedings/15/Papers/259.pdf

    2. S. Craw et al; Music recommenders: user evaluation without real users?;
      Proceedings of IJCAI 2015; 2015
      http://ijcai.org/Proceedings/15/Papers/249.pdf

    3. A. Rosenfeld et al.; Intelligent agent supporting human-multi-robot team collaboration;
      Proceedings of IJCAI 2015; 2015
      http://ijcai.org/Proceedings/15/Papers/270.pdf

    4. A. Azin et al.; Optimal greedy diversity for recommendation;
      Proceedings of IJCAI 2015; 2015
      http://ijcai.org/Proceedings/15/Papers/248.pdf

    5. G. Theocharous et al.; Building personal ad recommendation systems for life-time value optimization with guarantees;
      Proceedings of IJCAI 2015; 2015
      http://ijcai.org/Proceedings/15/Papers/257.pdf

    6. G. Guo et al.; TrustSVD: collaborative filtering with both the explicit and implicit influence of user trust and of item ratings;
      Proceedings of AAAI 2015; 2015
      http://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/view/9313

    7. W. Wu and L. Chen; Implicit acquisition of user personality for augmenting movie recommendations;
      Proceedings of UMAP 2015; 2015
      http://www.comp.hkbu.edu.hk/~lichen/download/Wu_UMAP15.pdf

    8. T. Bosse et al.; An adaptive human-aware software agent supporting attention-demanding tasks;
      Proceedings of PRIMA 2009; 2009
      http://www.few.vu.nl/~wai/Papers/PRIMA09attention.pdf

 

 

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