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Arcos JLluis, Mulayim O, Leake D.  2008.  Using Introspective Reasoning to Improve CBR System Performance. AAAI Metareasoning Workshop. :21-28.
Arcos JLluis, de Mántaras RLópez, Serra X.  1997.  SaxEx : a case-based reasoning system for generating expressive musical performances. International Computer Music Conference ICMC'97 (Best Paper Award). :329-336.
Argente E, Boissier O, Carrascosa C, Fornara N, Mcburney P, Noriega P, Ricci A, Sabater-Mir J., Schumacher MIgnaz, Tampitsikas C et al..  2012.  Environment and Agreement Technologies. Proceedings of the First International Conference on Agreement Technologies. :260-261.
Argente E, Boissier O, Carrascosa C, Fornara N, Mcburney P, Noriega P, Ricci A, Sabater-Mir J., Schumacher MIgnaz, Tampitsikas C et al..  2013.  The role of the environment in agreement technologies. Artificial Intelligence Review. 39:21-38.
Argerlich J., Manyà F.  2008.  A Preprocessor for Max-SAT Solvers. 11th International Conference on Theory and Applications of Satisfiability Testing (SAT-2008). 4996:15-20.
Argerlich J., Li CMin, Manyà F, Planes J.  2011.  Analyzing the Instances of the MaxSAT Evaluation. 14th International Conference on Theory and Applications of Satisfiability Testing, SAT 2011. 6695:360-361.
Argerlich J., Cabiscol A, Lynce I, Manyà F.  2008.  Modelling Max-CSP as Partial Max-SAT. 11th International Conference on Theory and Applications of Satisfiability Testing (SAT-2008). 4996:1-14.
Argerlich J., Manyà F.  2006.  Exact Max-SAT solvers for over-constrained problems. Journal of Heuristics. :375-392.
Argerlich J., Cabiscol A, Lynce I, Manyà F.  2009.  Sequential Encodings from Max-CSP into Partial Max-SAT. 12th International Conference on Theory and Applications of Satisfiability Testing (SAT 2009). LNCS 5584:161-166.
Argerlich J., Cabiscol A, Lynce I, Manyà F.  2010.  New Insights into Encodings from MaxCSP into Partial MaxSAT. 40th IEEE International Symposium on Multiple-Valued Logic (ISMVL). :46-52.
Argerlich J., Manyà F.  2005.  Solving over-constrained problems with SAT technology. Lecture Notes in Computer Science. :1-15.
Argerlich J., Domingo X, Li CMin, Manyà F, Planes J..  2006.  Towards Solving Many-Valued MaxSAT. Proceedings, 36th International Symposium on Multiple-Valued Logics (ISMVL-2006), Singapore.
Argerlich J., Li CMin, Manyà F, Planes J.  2008.  The First and Second Max-SAT Evaluations. Journal on Satisfiability, Boolean Modeling and Computation. 4:251-278.
Argerlich J., Cabiscol A, Lynce I, Manyà F.  2012.  Efficient Encodings from CSP into SAT, and from MaxCSP into MaxSAT. Multiple-Valued Logic and Soft Computing. 19:3-23.
Argerlich J., Li CMin, Manyà F, Planes J.  2011.  Experimenting with the Instances of the MaxSAT Evaluation. 14th International Conference of the Catalan Association for Artificial Intelligence. 232:31-40.
Armengol E, Plaza E.  2002.  Similarity of structured cases in CBR. Butlletí de L'ACIA. CCIA'2002. 5è Congrès Català d'Intel.ligència Artificial, Castelló, 24-25 d'Octubre del 2002.gs of the 5th Catalan Conference on Artificial Intelligence (CCIA'2002). :153-160.
Armengol E.  2000.  Explanation-based Learning.
Armengol E, Puig S.  2011.  Combining two lazy learning methods for classification and knowledge discovery.. International Conference on Knowledge Discovery and Information Retrieval.
Armengol E, Plaza E.  2003.  Discovery of toxicological patterns with lazy learning. Lecture Notes in Computer Science. Lecture Notes in Artificial Intelligence. 2774:919-926.
Armengol E, Dellunde P, García-Cerdaña A.  2015.  A Logical Study of Local and Global Graded Similarities. Applied Artificial Intelligence. 29:424-444.
Armengol E.  2011.  Classification of Melanomas in situ using Knowledge Discovery with Explained CBR. Artificial Intelligence in Medicine. 51:12.
Armengol E, Dellunde P, García-Cerdaña A.  2010.  On Similarities in Fuzzy Description Logics. Logic, Algebra and Truth Degrees 2010. 502:44-49.
Armengol E, Plaza E.  2005.  Lazy Learning for Predictive Toxicology based on a Chemical Ontology. Artificial Intelligence Methods and Tools for Systems Biology. :1-18.
Armengol E, Plaza E.  2005.  An ontological approach to represent molecular structure information. Lecture Notes in Computer Science. Lecture Notes in Bioinformatics. 3745:394-304.
Armengol E.  1998.  A Framework for Integrated Learning and Problem Solving.
Armengol E, Torra V.  2015.  Generalization-Based k-Anonymization. 5861:207-218.
Armengol E, Plaza E.  2003.  Remembering Similitude Terms in CBR. Machine Learning and Data Mining in Pattern Recognition (MLDM 2003). LNAI 2734:121-130.
Armengol E, García-Cerdaña A.  2010.  Lazy induction of descriptions using two fuzzy versions of the Rand index. IPMU 2010. 80:396-405.
Armengol E, Puertas E.  2006.  Improving individual learning capabilities in Multi-Agent Systems. Workshop on Adaptation and Learning in autonomous agents and multi-agent systems. AAMAS-2006.
Armengol E, Puyol-Gruart J.  2015.  A Simple Experiment to Guide the Design of a Preference Model. Artificial Intelligence Research and Development, Proceedings of the 18th International Conference of the Catalan Association for Artificial Intelligence. 277:59-68.
Armengol E, Plaza E.  2005.  Using Symbolic Similarity to Explain CBR in Classification Task. Fall Symposium. Proceedings AAAI. FS-05-04. :1-9.
Armengol E, Plaza E.  2003.  Relational Case-based Reasoning for Cancinogenic Activity Prediction. Artificial Intelligence Review. 20:121-141.
Armengol E, Plaza E.  1997.  Induction of Feature Terms with INDIE. Future Generation Computer Systems Journal. 12:173-188.
Armengol E, Puyol-Gruart J.  2017.  A reward-based approach for preference modeling: A case study. Journal of Applied Logic. 23:51-69.
Armengol E, Plaza E.  2004.  Multiple-instance case-based learning for predictive toxicology. Lecture Notes in Computer Science. 3303:206-220.
Armengol E, García-Cerdaña A.  2012.  Refining Discretizations of Continuous-Valued Attributes. The 9th International Conference on Modeling Decisions for Artificial Intelligence. 7647:258-269.
Armengol E, Ontañón S, Plaza E.  2004.  Explaining Similarity in CBR. Proceedigs of the ECCBR 2004 Workshops. 7th. European Conference on Case-Based Reasoning Madrid, Spain 30th. Augoust- 2nd September 2004. :87-95.
Armengol E, Plaza E.  2005.  Using Symbolic Descriptions to Explain Similarity on CBR. Artificial intelligence research and development (CCIA-2005). :239-246.
Armengol E.  2007.  Discovering plausible explanations of carcinogenenecity in chemical compounds. Lecture Notes in Artificial Intelligence. 4571:756-769.
Armengol E, Esteva F, Godo L, Torra V.  2004.  On learning similarity relations in fuzzy case-based reasoning. Lecture Notes in Computer Science. 3135:14-32.
Armengol E, Puertas E.  2006.  Learning from cooperation using justifications. Artificial Intelligence Research and Development. :47-54.
Armengol E, Dellunde P, García-Cerdaña A.  2012.  Towards a Fuzzy Extension of the López de Mántaras Distance. IPMU 2012. 297:81-90.
Armengol E.  2009.  Using explanations for determining carcinogenecity in chemical compounds. International Journal on Engineering Applications of Artificial Intelligence. 22:8.
Armengol E.  2007.  Usages of Generalization in CBR. Lecture Notes in Artificial Intelligence. :31-45.
Armengol E, Dellunde P, García-Cerdaña A.  2016.  On similarity in fuzzy description logics. Fuzzy Sets and Systems. 292:49–74.
Armengol E, Plaza E.  2006.  Symbolic explanation of similarities in case-based reasoning. Computing and Informatics. 25:153-171.
Armengol E, Plaza E.  2001.  Lazy Induction of Descriptions for Relational Case-Based Learning. Lecture Notes in Artificial Intelligence. 2167:13-24.
Armengol E, Plaza E.  1995.  Explanation-based Learning: A Knowledge Level Analysis. AI Review. 9:19-35.
Armengol E, Plaza E.  1995.  Integrating induction in a Case-based Reasoner. Lecture Notes in Artificial Intelligence. 984:3-17.
Armengol E.  1999.  Explanation-based Learning.