T. When, When to Act, and When to Chat, p.33

M. Alekhnovich, . Braverman, . Mark, . Feldman, . Vitaly et al., The complexity of properly learning simple concept classes, Journal of Computer and System Sciences, vol.74, issue.1, pp.16-34, 2008.
DOI : 10.1016/j.jcss.2007.04.011

J. Amilhastre, . Fargier, . Héì, and P. Marquis, Consistency restoration and explanations in dynamic CSPs???Application to configuration, Artificial Intelligence, vol.135, issue.1-2, pp.199-234, 2002.
DOI : 10.1016/S0004-3702(01)00162-X

D. Angluin, Queries and concept learning, Machine Learning, pp.319-342, 1988.
DOI : 10.1007/BF00116828

D. Angluin, Negative results for equivalence queries, Machine Learning, pp.121-150, 1990.
DOI : 10.1007/BF00116034

D. Angluin, . Frazier, . Michael, and L. Pitt, Learning conjunctions of Horn clauses, Machine Learning, pp.2-3147, 1992.

P. Auer and P. M. Long, Structural results about on-line learning models with and without queries, Machine Learning, vol.36, issue.3, pp.147-181, 1999.
DOI : 10.1023/A:1007614417594

I. Bahar, E. Frohm, C. Gaona, . Hachtel, . Gary et al., Algebric decision diagrams and their applications, Formal Methods in System Design, vol.10, pp.2-3171, 1997.

R. Barbanchon, ´. Grandjean, and . Etienne, The Minimal Logically-Defined NP-Complete Problem, Proc. 21st Annual Symposium on Theoretical Aspects of Computer Science Lecture Notes in Computer Science, pp.338-349, 2004.
DOI : 10.1007/978-3-540-24749-4_30

URL : https://hal.archives-ouvertes.fr/hal-00255837

O. Beyersdorff, . Meier, . Arne, . Thomas, . Michael et al., The complexity of propositional implication, Information Processing Letters, vol.109, issue.18, pp.1071-1077, 2009.
DOI : 10.1016/j.ipl.2009.06.015

. Biere, . Armin, . Heule, . Marijn, . Van-maaren et al., Handbook of Satisfiability. Number 185 in Frontiers in Artificial Intelligence and Applications, 2009.

A. Blum, . Hellerstein, . Lisa, and N. Littlestone, Learning in the Presence of Finitely or Infinitely Many Irrelevant Attributes, Journal of Computer and System Sciences, vol.50, issue.1, pp.32-40, 1995.
DOI : 10.1006/jcss.1995.1004

. Böhler, . Elmar, . Creignou, . Nadia, . Reith et al., Playing with Boolean blocks, part II: Constraint satisfaction problems, SIGACT News, vol.35, issue.1, pp.22-35, 2004.

´. Bonzon and . Elise, Modélisation des interactions entre agents rationnels : les jeux booléens, 2007.

M. Boussard, . Bouzid, . Maroua, . Mouaddib, . Abdel-illah et al., Non-Standard Criteria, Markov Decision Processes in Artificial Intelligence, chapter 10, pp.319-359, 2010.
DOI : 10.1002/9781118557426.ch10

URL : https://hal.archives-ouvertes.fr/hal-01291280

C. Boutilier, . Dean, . Thomas, and S. Hanks, Decision-theoretic planning: Structural assumptions and computational leverage, Journal of Artificial Intelligence Research, vol.11, pp.1-94, 1999.

C. Boutilier, K. Regan, and P. Viappiani, Simultaneous elicitation of preference features and utility, Proc. 24th AAAI Conference on Artificial Intelligence, pp.1160-1167, 2010.

R. I. Brafman and M. Tennenholtz, R-max ? A general polynomial time algorithm for near-optimal reinforcement learning, Journal of Machine Learning Research, vol.3, pp.213-231, 2002.

T. Brueggemann and W. Kern, An improved deterministic local search algorithm for 3-SAT, Theoretical Computer Science, vol.329, issue.1-3, pp.1-3303, 2004.
DOI : 10.1016/j.tcs.2004.08.002

N. H. Bshouty, Exact Learning Boolean Functions via the Monotone Theory, Information and Computation, vol.123, issue.1, pp.146-153, 1995.
DOI : 10.1006/inco.1995.1164

J. Byskov, . Makholm, B. Madsen, . Ammitzbøll, . Skjernaa et al., New algorithms for exact satisfiability, Theoretical Computer Science, vol.332, pp.1-3515, 2005.
DOI : 10.7146/brics.v10i30.21798

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

Y. Chevaleyre, . Koriche, . Frédéric, . Lang, . Jérôme et al., Learning Ordinal Preferences on Multiattribute Domains: The Case of CP-nets, Preference Learning, pp.273-296, 2010.
DOI : 10.1007/978-3-642-14125-6_13

URL : https://hal.archives-ouvertes.fr/hal-00944354

N. Creignou, . Khanna, . Sanjeev, and M. Sudan, Complexity classifications of Boolean constraint satisfaction problems, SIAM Monographs on Discrete Mathematics and Applications . SIAM, 2001.
DOI : 10.1137/1.9780898718546

N. Creignou, . Kolaitis, G. Phokion, and V. , Complexity of Constraints, Heribert Lecture Notes in Computer Science, vol.5250, 2008.
DOI : 10.1007/978-3-540-92800-3

URL : https://hal.archives-ouvertes.fr/hal-01194339

N. Creignou, . Kolaitis, G. Phokion, and B. Zanuttini, Structure identification of Boolean relations and plain bases for co-clones, Journal of Computer and System Sciences, vol.74, issue.7, pp.1103-1115, 2008.
DOI : 10.1016/j.jcss.2008.02.005

URL : https://hal.archives-ouvertes.fr/hal-00995233

N. Creignou, J. Schmidt, and M. Thomas, Complexity of propositional abduction for restricted sets of Boolean functions, Proc. 12th International Conference on Principles of Knowledge Representation and Reasoning, pp.8-16, 2010.
URL : https://hal.archives-ouvertes.fr/hal-01195496

N. Creignou, . Schmidt, . Johannes, . Thomas, . Michael et al., Sets of Boolean Connectives That Make Argumentation Easier, Proc. 12th European Conference on Logics in Artificial Intelligence, pp.117-129, 2010.
DOI : 10.1007/978-3-642-15675-5_12

URL : https://hal.archives-ouvertes.fr/hal-01195476

N. Creignou and B. Zanuttini, A Complete Classification of the Complexity of Propositional Abduction, SIAM Journal on Computing, vol.36, issue.1, pp.207-229, 2006.
DOI : 10.1137/S0097539704446311

URL : https://hal.archives-ouvertes.fr/hal-00197028

V. Dalmau, A dichotomy theorem for learning quantified Boolean formulas, Proceedings of the tenth annual conference on Computational learning theory , COLT '97, pp.207-224, 1999.
DOI : 10.1145/267460.267496

. Dantsin, . Evgeny, . Goerdt, . Andreas, E. A. Hirsch et al., A deterministic (2???2/(k+1))n algorithm for k-SAT based on local search, Theoretical Computer Science, vol.289, issue.1, pp.69-83, 2002.
DOI : 10.1016/S0304-3975(01)00174-8

A. Darwiche and P. Marquis, A knowledge compilation map, Journal of Artificial Intelligence Research, vol.17, pp.229-264, 2002.

T. Degris, . Sigaud, . Olivier, and P. Wuillemin, Learning the structure of Factored Markov Decision Processes in reinforcement learning problems, Proceedings of the 23rd international conference on Machine learning , ICML '06, pp.257-264, 2006.
DOI : 10.1145/1143844.1143877

URL : https://hal.archives-ouvertes.fr/hal-01336925

A. Del-val, An analysis of approximate knowledge compilation, Proc. 14th International Joint Conference on Artificial Intelligence, pp.830-836, 1995.

Y. Dimopoulos, . Michael, . Loizos, . Athienitou, and . Fani, Ceteris Paribus preference elicitation with predictive guarantees, Proc. 21st International Joint Conference on Artificial Intelligence, pp.1890-1895, 2009.

C. Domshlak and R. I. Brafman, CP-nets ? reasoning and consistency testing, Proc. 8th International Conference on Principles of Knowledge Representation and Reasoning, pp.121-132, 2002.

A. Durand, . Hermann, . Miki, and G. Nordh, Trichotomy in the complexity of minimal inference, Proc. 24th Annual IEEE Symposium on Logic in Computer Science, pp.387-396, 2009.

S. D?eroski, D. Raedt, . Luc, and K. Driessens, Relational reinforcement learning, Machine Learning, pp.7-52, 2001.
DOI : 10.1007/BFb0027307

T. Eiter and G. Gottlob, The complexity of logic-based abduction, Journal of the ACM, vol.42, issue.1, pp.3-42, 1995.
DOI : 10.1145/200836.200838

H. Fargier, . Ene, and P. Marquis, Extending the knowledge compilation map: Krom, Horn, affine and beyond, Proc. 23rd AAAI Conference on Artificial Intelligence, pp.442-447, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00800742

H. Fargier, . Ene, and P. Marquis, Knowledge compilation properties of tress-of-BDDs, revisited, Proc. 21st International Joint Conference on Artificial Intelligence, pp.772-777, 2009.

Z. Feng and E. A. Hansen, Symbolic heuristic search for factored Markov decision processes, Proc. 18th National Conference on Artificial Intelligence, pp.455-460, 2002.

J. Fürnkranz and E. Hüllermeier, Preference Learning, 2010.
DOI : 10.1007/978-1-4899-7502-7_667-1

F. Garcia and E. Rachelson, Markov Decision Processes, Markov Decision Processes in Artificial Intelligence, chapter 1, pp.3-38, 2010.
DOI : 10.1002/9781118557426.ch1

R. Givan, . Dean, . Thomas, and M. Greig, Equivalence notions and model minimization in Markov decision processes, Artificial Intelligence, vol.147, issue.1-2, pp.163-223, 2003.
DOI : 10.1016/S0004-3702(02)00376-4

D. Gordon and D. Perlis, Explicitly biased generalization, Goal-Driven Learning, chapter 13, pp.321-354, 1995.
DOI : 10.1145/1968.1972

C. Guestrin, . Koller, . Daphne, R. Parr, and S. Venkataraman, Efficient solution algorithms for factored MDPs, Journal of Artificial Intelligence Research, vol.19, pp.399-468, 2003.

L. Haddad and G. E. Simons, On intervals of partial clones of boolean partial functions, 33rd International Symposium on Multiple-Valued Logic, 2003. Proceedings., pp.315-322, 2003.
DOI : 10.1109/ISMVL.2003.1201423

S. Haykin, Neural Networks ? A Comprehensive Foundation, 1999.

J. Hébrard and B. Zanuttini, An efficient algorithm for Horn description, Information Processing Letters, vol.88, issue.4, pp.177-182, 2003.
DOI : 10.1016/j.ipl.2003.06.001

M. Hermann and R. Pichler, Counting complexity of propositional abduction, Journal of Computer and System Sciences, vol.76, issue.7, pp.634-649, 2010.
DOI : 10.1016/j.jcss.2009.12.001

J. Hoey, . St-aubin, . Robert, A. J. Hu, and C. Boutilier, SPUDD: Stochastic planning using decision diagrams, Proc. 15th Conference on Uncertainty in Artificial Intelligence, pp.279-288, 1999.

P. Jeavons, . Cohen, . David, and M. Gyssens, Closure properties of constraints, Journal of the ACM, vol.44, issue.4, pp.527-548, 1997.
DOI : 10.1145/263867.263489

L. Kaelbling, . Pack, . Littman, L. Michael, C. et al., Planning and acting in partially observable stochastic domains, Artificial Intelligence, vol.101, issue.1-2, pp.99-134, 1998.
DOI : 10.1016/S0004-3702(98)00023-X

S. Kakade and . Machandranath, On the sample complexity of reinforcement learning, 2003.

D. J. Kavvadias and M. Sideri, The Inverse Satisfiability Problem, SIAM Journal on Computing, vol.28, issue.1, pp.152-163, 1998.
DOI : 10.1137/S0097539795285114

M. Kearns and D. Koller, Efficient reinforcement learning in factored MDPs, Proc. 16th International Joint Conference on Artificial Intelligence (IJCAI 1999), pp.740-474, 1999.

M. Kearns, . Mansour, . Yishay, and A. Y. Ng, A sparse sampling algorithm for near-optimal planning in large Markov decision processes, Machine Learning, pp.193-208, 2002.

M. Kearns and S. Singh, Near-optimal reinforcement learning in polynomial time, Machine Learning, pp.2-3209, 2002.

M. J. Kearns and R. E. Schapire, Efficient distribution-free learning of probabilistic concepts, Journal of Computer and System Sciences, vol.48, issue.3, pp.464-497, 1994.
DOI : 10.1016/S0022-0000(05)80062-5

M. J. Kearns, R. E. Schapire, and L. M. Sellie, Toward efficient agnostic learning, Machine Learning, pp.115-141, 1994.
DOI : 10.1145/130385.130424

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

K. Kersting, V. Otterlo, D. Martijn, and L. Raedt, Bellman goes relational, Twenty-first international conference on Machine learning , ICML '04, pp.465-472, 2004.
DOI : 10.1145/1015330.1015401

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

R. Khardon and D. Roth, Reasoning with models, Artificial Intelligence, vol.87, issue.1-2, pp.187-213, 1996.
DOI : 10.1016/S0004-3702(96)00006-9

F. Koriche and B. Zanuttini, Learning conditional preference networks with queries, Proc. 21st International Joint Conference on Artificial Intelligence, pp.1930-1935, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00944391

F. Koriche and B. Zanuttini, Learning conditional preference networks, Artificial Intelligence, vol.174, issue.11, pp.685-703, 2010.
DOI : 10.1016/j.artint.2010.04.019

URL : https://hal.archives-ouvertes.fr/lirmm-00485498

N. Kushmerick, . Hanks, . Steve, . Weld, and S. Daniel, An algorithm for probabilistic planning, Artificial Intelligence, vol.76, issue.1-2, pp.239-286, 1995.
DOI : 10.1016/0004-3702(94)00087-H

J. Lang and J. Mengin, The complexity of learning separable ceteris paribus preferences, Proc. 21st International Joint Conference on Artificial Intelligence (IJCAI'09), pp.848-853, 2009.

T. Lang and M. Toussaint, Planning with noisy probabilistic relational rules, Journal of Artificial Intelligence Research, vol.39, pp.1-49, 2010.

D. Lau, Function Algebras on Finite Sets ? A basic course on many-valued logic and clone theory, 2006.

B. Lesner and B. Zanuttini, Apprentissage par renforcement de pdm factorisés avec effets corrélés, Proc. 5es Journées Francophones Planification Décision Apprentissage, 2010.

B. Lesner and B. Zanuttini, Résolution exacte et approchée deprobì emes de décision markoviens formulés en logique propositionnelle. Revue d'intelligence artificielle, pp.131-158, 2010.
DOI : 10.3166/ria.24.131-158

URL : https://hal.archives-ouvertes.fr/hal-00947046/file/hal-00947046.pdf

B. Lesner and B. Zanuttini, Efficient policy construction for MDPs represented in probabilistic PDDL, Bacchus Proc. 21st International Conference on Automated Planning and Scheduling, pp.146-153, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00944350

L. Li, . Littman, L. Michael, T. J. Walsh, and A. L. Strehl, Knows what it knows, Proceedings of the 25th international conference on Machine learning, ICML '08, pp.399-443, 2011.
DOI : 10.1145/1390156.1390228

P. Liberatore, Compilation of Intractable Problems and Its Application to Artificial Intelligence, 1998.

N. Littlestone, Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm, 28th Annual Symposium on Foundations of Computer Science (sfcs 1987), pp.285-318, 1988.
DOI : 10.1109/SFCS.1987.37

M. L. Littman, Probabilistic propositional planning: Representations and complexity, Proc. 14th National Conference on Artificial Intelligence, pp.748-754, 1997.

J. Mccarthy, Circumscription???A form of non-monotonic reasoning, Artificial Intelligence, vol.13, issue.1-2, pp.27-39, 1980.
DOI : 10.1016/0004-3702(80)90011-9

J. Mccarthy, Applications of circumscription to formalizing common-sense knowledge, Artificial Intelligence, vol.28, issue.1, pp.89-116, 1986.
DOI : 10.1016/0004-3702(86)90032-9

T. M. Mitchell, The need for biases in learning generalizations, 1980.

T. M. Mitchell, Generalization as search, Artificial Intelligence, vol.18, issue.2, pp.203-226, 1982.
DOI : 10.1016/0004-3702(82)90040-6

B. Monien and E. Speckenmeyer, Solving satisfiability in less than 2n steps, Discrete Applied Mathematics, vol.10, issue.3, pp.287-295, 1985.
DOI : 10.1016/0166-218X(85)90050-2

URL : http://doi.org/10.1016/0166-218x(85)90050-2

. Natarajan and K. Balas, Machine Learning ? A theoretical approach, 1991.

G. Nordh, A Trichotomy in the Complexity of Propositional Circumscription, Proc. 11th International Conference on Logic for Programming , Artificial Intelligence and Reasoning, pp.257-269, 2004.
DOI : 10.1007/978-3-540-32275-7_18

G. Nordh and B. Zanuttini, Propositional abduction is almost always hard, Proc. 19th International Joint Conference on Artificial Intelligence (IJCAI 2005), pp.534-539, 2005.
URL : https://hal.archives-ouvertes.fr/hal-00263492

G. Nordh and B. Zanuttini, What makes propositional abduction tractable, Artificial Intelligence, vol.172, issue.10, pp.1245-1284, 2008.
DOI : 10.1016/j.artint.2008.02.001

URL : https://hal.archives-ouvertes.fr/hal-00995230

G. Nordh and B. Zanuttini, Frozen Boolean Partial Co-clones, 2009 39th International Symposium on Multiple-Valued Logic, pp.120-125, 2009.
DOI : 10.1109/ISMVL.2009.10

URL : https://hal.archives-ouvertes.fr/hal-00944410

H. M. Pasula, L. S. Zettlemoyer, L. Kaelbling, and . Pack, Learning symbolic models of stochastic domains, Journal of Artificial Intelligence Research, vol.29, pp.309-352, 2007.

E. Post, The two-valued iterative systems of mathematical logic, Annals of Mathematical Studies, vol.5, pp.1-122, 1941.
DOI : 10.1515/9781400882366

M. L. Puterman, Markov Decision Processes ? Discrete Stochastic Dynamic Programming, 1994.

J. Rintanen, Expressive equivalence of formalism for planning with sensing, Proc. 13th International Conference on Automated Planning and Scheduling (ICAPS 2003), pp.185-194, 2003.

C. Rodrigues, . Gérard, . Pierre, . Rouveirol, . Céline et al., Incremental Learning of Relational Action Rules, 2010 Ninth International Conference on Machine Learning and Applications, pp.451-458, 2010.
DOI : 10.1109/ICMLA.2010.73

F. Rossi, . Van-beek, . Peter, and T. Walsh, Handbook of Constraint Programming, Foundations of Artificial Intelligence, 2006.

M. P. Sachdev, On learning of ceteris paribus preference theories, 2007.

J. Safaei, . Ghassem-sani, . Gholamreza, G. F. Italiano, and . Van-der-hoek, Incremental Learning of Planning Operators in Stochastic Domains, van Leeuwen Proc. 33rd Conference on Current Trends in Theory and Practice of Computer Science, pp.644-655, 2007.
DOI : 10.1007/978-3-540-69507-3_56

S. Sanner and C. Boutilier, Practical solution techniques for first-order MDPs, Artificial Intelligence, vol.173, issue.5-6, pp.5-6748, 2009.
DOI : 10.1016/j.artint.2008.11.003

S. Sanner and D. Mcallester, Affine algebraic decision diagrams (AADDs) and their application to structured probabilistic inference, Proc. 19th International Joint Conference on Artificial Intelligence (IJCAI 2005), pp.1384-1390, 2005.

F. Scarcello, . Gottlob, . Georg, and G. Greco, Uniform Constraint Satisfaction Problems and Database Theory, Complexity of Constraints, pp.156-195, 2008.
DOI : 10.1016/0022-0000(84)90007-2

. Schachte, . Peter, . Søndergaard, . Harald, . Whiting et al., Information loss in knowledge compilation: A comparison of Boolean envelopes, Artificial Intelligence, vol.174, issue.9-10, pp.9-10585, 2010.
DOI : 10.1016/j.artint.2010.03.003

T. J. Schaefer, The complexity of satisfiability problems, Proceedings of the tenth annual ACM symposium on Theory of computing , STOC '78, pp.216-226, 1978.
DOI : 10.1145/800133.804350

H. Schnoor and I. Schnoor, Partial Polymorphisms and Constraint Satisfaction Problems, Creignou Complexity of Constraints, pp.229-254, 2008.
DOI : 10.1145/321864.321877

M. Sebag, Delaying the choice of bias: a disjunctive version space approach, Proc. 13th International Conference on Machine Learning, pp.444-152, 1996.
URL : https://hal.archives-ouvertes.fr/hal-00116418

B. Selman and H. Kautz, Knowledge compilation and theory approximation, Journal of the ACM, vol.43, issue.2, pp.193-224, 1996.
DOI : 10.1145/226643.226644

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

M. P. Shanahan, The Event Calculus Explained, Artificial Intelligence Today, number 1600 in Lecture Notes in Artificial Intelligence, pp.409-430, 1999.
DOI : 10.1016/S0004-3702(96)00033-1

. St-aubin, . Robert, J. Hoey, and C. Boutilier, APRICODD: Approximate policy construction using decision diagrams, Proc. 13th Annual Conference on Neural Informatino Processing Systems, pp.1089-1095, 2000.

A. L. Strehl, C. Diuk, . Littman, and L. Michael, Efficient structure learning in factored-state MDPs, Proc. 22nd AAAI Conference on Artificial Intelligence, pp.645-650, 2007.

A. L. Strehl, . Li, . Lihong, . Littman, and L. Michael, Incremental model-based learners with formal learning-time guarantees, Proc. 22nd Conference on Uncertainty in Artificial Intelligence, 2006.

A. L. Strehl, . Littman, and L. Michael, An analysis of model-based Interval Estimation for Markov Decision Processes, Journal of Computer and System Sciences, vol.74, issue.8, pp.1309-1331, 2008.
DOI : 10.1016/j.jcss.2007.08.009

R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, IEEE Transactions on Neural Networks, vol.9, issue.5, 1998.
DOI : 10.1109/TNN.1998.712192

P. E. Utgoff, Shift of bias for inductive concept learning, Machine Learning, pp.107-148, 1986.

L. Valiant, A theory of the learnable, Communications of the ACM, vol.27, issue.11, pp.1134-1142, 1984.
DOI : 10.1145/1968.1972

T. Walsh, SAT v CSP, Proc. 6th International Conference on Principles and Practice of Constraint Programming, pp.441-456, 2000.
DOI : 10.1007/3-540-45349-0_32

T. J. Walsh, . Szita, . István, C. Diuk, . Littman et al., Exploring compact reinforcement-learning representations with linear regression, Proc. 25th Conference on Uncertainty in Artificial Intelligence, 2009.

C. Wang, . Joshi, . Saket, and R. Khardon, First order decision diagrams for relational MDPs, Journal of Artificial Intelligence Research, vol.31, pp.431-472, 2008.

. Weissman, . Tsachy, . Ordentlich, . Erik, . Seroussi et al., Inequalities for the l 1 deviation of the empirical distribution, 2003.

P. Weng, Markov decision processes with ordinal rewards: Reference point-based preferences, Proc. 21st International Conference on Automated Planning and Scheduling, 2011.
URL : https://hal.archives-ouvertes.fr/hal-01285812

H. L. Younes, . Littman, and L. Michael, PPDDL1.0: An extension to PDDL for expressing planning domains with probabilistic effects, 2004.

B. Zanuttini, Approximating propositional knowledge with affine formulas, Proc. 15th European Conference on Artificial Intelligence, pp.287-291, 2002.
URL : https://hal.archives-ouvertes.fr/hal-00995239

B. Zanuttini, Approximation of Relations by Propositional Formulas: Complexity and Semantics, Proc. 5th Symposium on Abstraction, Reformulation and Approximation number 2371 in Lecture Notes in Artificial Intelligence, pp.242-255, 2002.
DOI : 10.1007/3-540-45622-8_18

URL : https://hal.archives-ouvertes.fr/hal-00995238

B. Zanuttini and J. Hébrard, A unified framework for structure identification, Information Processing Letters, vol.81, issue.6, pp.335-339, 2002.
DOI : 10.1016/S0020-0190(01)00247-2

URL : https://hal.archives-ouvertes.fr/hal-00995240