e-Tourism: a tourist recommendation and planning application

e-Tourism is a tourist recommendation and planning application to assist users on the organization of a leisure and tourist agenda. First, a recommender system offers the user a list of the city places that are likely of interest to the user. This list takes into account the user demographic classification, the user likes in former trips and the preferences for the current visit. Second, a planning module schedules the list of recommended places according to their temporal characteristics as well as the user restrictions; that is the planning system determines how and when to realize the recommended activities. Having the list of recommended activities organized as an agenda (i.e. an executable plan), is a relevant characteristic that most recommender systems lack.This work has been partially funded by Consolider Ingenio 2010 CSD2007-00022 project, by the Spanish Government MICINN TIN2008-6701-C03-01 project and by the Valencian Government GVPRE/2008/384 project. We thank J. Benton for having provided us with the system Sapa to execute our experiments.Sebastiá Tarín, L.; García García, I.; Onaindia De La Rivaherrera, E.; Gúzman Álvarez, CA. (2009). e-Tourism: a tourist recommendation and planning application. International Journal on Artificial Intelligence Tools. 18(5):717-738. https://doi.org/10.1142/S0218213009000378S71773818

Prevalence of Systemic Lupus Erythematosus in Spain: Higher than Previously Reported in other Countries?

[Abstract] Objectives: Prevalence of SLE varies among studies, being influenced by study design, geographical area and ethnicity. Data about the prevalence of SLE in Spain are scarce. In the EPISER2016 study, promoted by the Spanish Society of Rheumatology, the prevalence estimate of SLE in the general adult population in Spain has been updated and its association with sociodemographic, anthropometric and lifestyle variables has been explored. Methods: Population-based multicentre cross-sectional study, with multistage stratified and cluster random sampling. Participants were contacted by telephone to carry out a questionnaire for the screening of SLE. Investigating rheumatologists evaluated positive results (review of medical records and/or telephone interview, with medical visit if needed) to confirm the diagnosis. To calculate the prevalence and its 95% CI, the sample design was taken into account and weighing was calculated considering age, sex and geographic origin. Multivariate logistic regression models were defined to analyse which sociodemographic, anthropometric and lifestyle variables included in the telephone questionnaire were associated with the presence of SLE. Results: 4916 subjects aged 20 years or over were included. 16.52% (812/4916) had a positive screening result for SLE. 12 cases of SLE were detected. The estimated prevalence was 0.21% (95% CI: 0.11, 0.40). SLE was more prevalent in the rural municipalities, with an odds ratio (OR) = 4.041 (95% CI: 1.216, 13.424). Conclusion: The estimated prevalence of SLE in Spain is higher than that described in most international epidemiological studies, but lower than that observed in ethnic minorities in the United States or the United Kingdom

FMAP: Distributed Cooperative Multi-Agent Planning

This paper proposes FMAP (Forward Multi-Agent Planning), a fully-distributed multi-agent planning method that integrates planning and coordination. Although FMAP is specifically aimed at solving problems that require cooperation among agents, the flexibility of the domain-independent planning model allows FMAP to tackle multi-agent planning tasks of any type. In FMAP, agents jointly explore the plan space by building up refinement plans through a complete and flexible forward-chaining partial-order planner. The search is guided by h D T G , a novel heuristic function that is based on the concepts of Domain Transition Graph and frontier state and is optimized to evaluate plans in distributed environments. Agents in FMAP apply an advanced privacy model that allows them to adequately keep private information while communicating only the data of the refinement plans that is relevant to each of the participating agents. Experimental results show that FMAP is a general-purpose approach that efficiently solves tightly-coupled domains that have specialized agents and cooperative goals as well as loosely-coupled problems. Specifically, the empirical evaluation shows that FMAP outperforms current MAP systems at solving complex planning tasks that are adapted from the International Planning Competition benchmarks.This work has been partly supported by the Spanish MICINN under projects Consolider Ingenio 2010 CSD2007-00022 and TIN2011-27652-C03-01, the Valencian Prometeo project II/2013/019, and the FPI-UPV scholarship granted to the first author by the Universitat Politecnica de Valencia.Torreño Lerma, A.; Onaindia De La Rivaherrera, E.; Sapena Vercher, O. (2014). FMAP: Distributed Cooperative Multi-Agent Planning. Applied Intelligence. 41(2):606-626. https://doi.org/10.1007/s10489-014-0540-2S606626412Benton J, Coles A, Coles A (2012) Temporal planning with preferences and time-dependent continuous costs. In: Proceedings of the 22nd international conference on automated planning and scheduling (ICAPS). AAAI, pp 2–10Borrajo D. (2013) Multi-agent planning by plan reuse. In: Proceedings of the 12th international conference on autonomous agents and multi-agent systems (AAMAS). IFAAMAS, pp 1141–1142Boutilier C, Brafman R (2001) Partial-order planning with concurrent interacting actions. J Artif Intell Res 14(105):136Brafman R, Domshlak C (2008) From one to many: planning for loosely coupled multi-agent systems. In: Proceedings of the 18th international conference on automated planning and scheduling (ICAPS). AAAI, pp 28–35Brenner M, Nebel B (2009) Continual planning and acting in dynamic multiagent environments. J Auton Agents Multiagent Syst 19(3):297–331Bresina J, Dearden R, Meuleau N, Ramakrishnan S, Smith D, Washington R (2002) Planning under continuous time and resource uncertainty: a challenge for AI. In: Proceedings of the 18th conference on uncertainty in artificial intelligence (UAI). Morgan Kaufmann, pp 77–84Cox J, Durfee E (2009) Efficient and distributable methods for solving the multiagent plan coordination problem. Multiagent Grid Syst 5(4):373–408Crosby M, Rovatsos M, Petrick R (2013) Automated agent decomposition for classical planning. In: Proceedings of the 23rd international conference on automated planning and scheduling (ICAPS). AAAI, pp 46–54Dimopoulos Y, Hashmi MA, Moraitis P (2012) μ-satplan: Multi-agent planning as satisfiability. Knowl-Based Syst 29:54–62Fikes R, Nilsson N (1971) STRIPS: a new approach to the application of theorem proving to problem solving. Artif Intell 2(3):189–208Gerevini A, Haslum P, Long D, Saetti A, Dimopoulos Y (2009) Deterministic planning in the fifth international planning competition: PDDL3 and experimental evaluation of the planners. Artif Intell 173(5-6):619–668Ghallab M, Nau D, Traverso P (2004) Automated planning. Theory and practice. Morgan KaufmannGünay A, Yolum P (2013) Constraint satisfaction as a tool for modeling and checking feasibility of multiagent commitments. Appl Intell 39(3):489–509Helmert M (2004) A planning heuristic based on causal graph analysis. In: Proceedings of the 14th international conference on automated planning and scheduling ICAPS. AAAI, pp 161–170Hoffmann J, Nebel B (2001) The FF planning system: fast planning generation through heuristic search. J Artif Intell Res 14:253–302Jannach D, Zanker M (2013) Modeling and solving distributed configuration problems: a CSP-based approach. IEEE Trans Knowl Data Eng 25(3):603–618Jonsson A, Rovatsos M (2011) Scaling up multiagent planning: a best-response approach. In: Proceedings of the 21st international conference on automated planning and scheduling (ICAPS). AAAI, pp 114–121Kala R, Warwick K (2014) Dynamic distributed lanes: motion planning for multiple autonomous vehicles. Appl Intell:1–22Koehler J, Ottiger D (2002) An AI-based approach to destination control in elevators. AI Mag 23(3):59–78Kovacs DL (2011) Complete BNF description of PDDL3.1. Technical reportvan der Krogt R (2009) Quantifying privacy in multiagent planning. Multiagent Grid Syst 5(4):451–469Kvarnström J (2011) Planning for loosely coupled agents using partial order forward-chaining. In: Proceedings of the 21st international conference on automated planning and scheduling (ICAPS). AAAI, pp 138–145Lesser V, Decker K, Wagner T, Carver N, Garvey A, Horling B, Neiman D, Podorozhny R, Prasad M, Raja A et al (2004) Evolution of the GPGP/TAEMS domain-independent coordination framework. Auton Agents Multi-Agent Syst 9(1–2):87–143Long D, Fox M (2003) The 3rd international planning competition: results and analysis. J Artif Intell Res 20:1–59Nissim R, Brafman R, Domshlak C (2010) A general, fully distributed multi-agent planning algorithm. In: Proceedings of the 9th international conference on autonomous agents and multiagent systems (AAMAS). IFAAMAS, pp 1323–1330O’Brien P, Nicol R (1998) FIPA - towards a standard for software agents. BT Tech J 16(3):51–59Öztürk P, Rossland K, Gundersen O (2010) A multiagent framework for coordinated parallel problem solving. Appl Intell 33(2):132–143Pal A, Tiwari R, Shukla A (2013) Communication constraints multi-agent territory exploration task. Appl Intell 38(3):357–383Richter S, Westphal M (2010) The LAMA planner: guiding cost-based anytime planning with landmarks. J Artif Intell Res 39(1):127–177de la Rosa T, García-Olaya A, Borrajo D (2013) A case-based approach to heuristic planning. Appl Intell 39(1):184–201Sapena O, Onaindia E (2008) Planning in highly dynamic environments: an anytime approach for planning under time constraints. Appl Intell 29(1):90–109Sapena O, Onaindia E, Garrido A, Arangú M (2008) A distributed CSP approach for collaborative planning systems. Eng Appl Artif Intell 21(5):698–709Serrano E, Such J, Botía J, García-Fornes A (2013) Strategies for avoiding preference profiling in agent-based e-commerce environments. Appl Intell:1–16Smith D, Frank J, Jónsson A (2000) Bridging the gap between planning and scheduling. Knowl Eng Rev 15(1):47–83Such J, García-Fornes A, Espinosa A, Bellver J (2012) Magentix2: a privacy-enhancing agent platform. Eng Appl Artif Intell:96–109Tonino H, Bos A, de Weerdt M, Witteveen C (2002) Plan coordination by revision in collective agent based systems. Artif Intell 142(2):121–145Torreño A, Onaindia E, Sapena O (2012) An approach to multi-agent planning with incomplete information. In: Proceedings of the 20th European conference on artificial intelligence (ECAI), vol 242. IOS Press, pp 762–767Torreño A, Onaindia E, Sapena O (2014) A flexible coupling approach to multi-agent planning under incomplete information. Knowl Inf Syst 38(1):141–178Van Der Krogt R, De Weerdt M (2005) Plan repair as an extension of planning. In: Proceedings of the 15th international conference on automated planning and scheduling (ICAPS). AAAI, pp 161–170de Weerdt M, Clement B (2009) Introduction to planning in multiagent systems. Multiagent Grid Syst 5(4):345– 355Yokoo M, Durfee E, Ishida T, Kuwabara K (1998) The distributed constraint satisfaction problem: formalization and algorithms. IEEE Trans Knowl Data Eng 10(5):673–685Zhang J, Nguyen X, Kowalczyk R (2007) Graph-based multi-agent replanning algorithm. In: Proceedings of the 6th international joint conference conference on autonomous agents and multiagent systems (AAMAS). IFAAMAS, pp 798–80

El papel de los estudios bioarqueológicos en las interpretaciones sobre las comunidades neolíticas del noreste peninsular

Resumen del trabajo presentado al VI Congreso del Neolítico en la Península Ibérica: "Los cambios económicos y sus implicaciones sociales durante el Neolítico de la Península Ibérica", celebrado en Granada del 22 al 26 de junio de 2016.-- et al.El marco del proyecto I+D: “Aproximación a las primeras comunidades neolíticas del NE peninsular a través de sus prácticas funerarias” (2011-2015), y su continuidad para los próximos cuatro años al haber sido renovado, tiene por objetivo conocer cada día más y mejor las comunidades de agricultores y pastores que entre finales del V e inicios del IV milenio cal BC ocuparon y enterraron a sus muertos en el noreste de la Península Ibérica. El contexto de estudio es excepcional, puesto que aquellas comunidades inhumaron sistemáticamente a sus congéneres en tumbas habitualmente individuales y ocasionalmente junto a otro individuo. En esta presentación no sólo queremos mostrar los nuevos análisis y metodologías que estamos aplicando al estudio de los restos humanos, sino también el modelo de trabajo que hemos seguido. A este respecto, tres aspectos son fundamentales: 1) las dataciones absolutas son el eje que vertebra los posteriores análisis; 2) la colaboración con los distintos investigadores/as y laboratorios debe ser estrecha (no es cuestión de solicitar los resultados de un análisis a un laboratorio sino trabajar con las personas que manipulan las muestras y conocen los pros y contras de cada una de las técnicas empleadas) y 3) los estudios y análisis a realizar confluyen para responder a las hipótesis planteadas. A este respecto, en el proyecto hemos tenido la fortuna de poder colaborar con numerosos investigadores/as cuya especialidad versa alrededor de los restos funerarios y que firman la presente comunicación: análisis isotópicos, Adn, tafonomía funeraria, estudios de stress muscular y análisis de morfología dental.Peer Reviewe

New prioritized value iteration for Markov decision processes

The problem of solving large Markov decision processes accurately and quickly is challenging. Since the computational effort incurred is considerable, current research focuses on finding superior acceleration techniques. For instance, the convergence properties of current solution methods depend, to a great extent, on the order of backup operations. On one hand, algorithms such as topological sorting are able to find good orderings but their overhead is usually high. On the other hand, shortest path methods, such as Dijkstra's algorithm which is based on priority queues, have been applied successfully to the solution of deterministic shortest-path Markov decision processes. Here, we propose an improved value iteration algorithm based on Dijkstra's algorithm for solving shortest path Markov decision processes. The experimental results on a stochastic shortest-path problem show the feasibility of our approach. © Springer Science+Business Media B.V. 2011.García Hernández, MDG.; Ruiz Pinales, J.; Onaindia De La Rivaherrera, E.; Aviña Cervantes, JG.; Ledesma Orozco, S.; Alvarado Mendez, E.; Reyes Ballesteros, A. (2012). New prioritized value iteration for Markov decision processes. Artificial Intelligence Review. 37(2):157-167. doi:10.1007/s10462-011-9224-zS157167372Agrawal S, Roth D (2002) Learning a sparse representation for object detection. In: Proceedings of the 7th European conference on computer vision. Copenhagen, Denmark, pp 1–15Bellman RE (1954) The theory of dynamic programming. Bull Amer Math Soc 60: 503–516Bellman RE (1957) Dynamic programming. Princeton University Press, New JerseyBertsekas DP (1995) Dynamic programming and optimal control. Athena Scientific, MassachusettsBhuma K, Goldsmith J (2003) Bidirectional LAO* algorithm. In: Proceedings of indian international conferences on artificial intelligence. p 980–992Blackwell D (1965) Discounted dynamic programming. Ann Math Stat 36: 226–235Bonet B, Geffner H (2003a) Faster heuristic search algorithms for planning with uncertainty and full feedback. In: Proceedings of the 18th international joint conference on artificial intelligence. Morgan Kaufmann, Acapulco, México, pp 1233–1238Bonet B, Geffner H (2003b) Labeled RTDP: improving the convergence of real-time dynamic programming. In: Proceedings of the international conference on automated planning and scheduling. Trento, Italy, pp 12–21Bonet B, Geffner H (2006) Learning depth-first search: a unified approach to heuristic search in deterministic and non-deterministic settings and its application to MDP. In: Proceedings of the 16th international conference on automated planning and scheduling. Cumbria, UKBoutilier C, Dean T, Hanks S (1999) Decision-theoretic planning: structural assumptions and computational leverage. J Artif Intell Res 11: 1–94Chang I, Soo H (2007) Simulation-based algorithms for Markov decision processes Communications and control engineering. Springer, LondonDai P, Goldsmith J (2007a) Faster dynamic programming for Markov decision processes. Technical report. Doctoral consortium, department of computer science and engineering. University of WashingtonDai P, Goldsmith J (2007b) Topological value iteration algorithm for Markov decision processes. In: Proceedings of the 20th international joint conference on artificial intelligence. Hyderabad, India, pp 1860–1865Dai P, Hansen EA (2007c) Prioritizing bellman backups without a priority queue. In: Proceedings of the 17th international conference on automated planning and scheduling, association for the advancement of artificial intelligence. Rhode Island, USA, pp 113–119Dibangoye JS, Chaib-draa B, Mouaddib A (2008) A Novel prioritization technique for solving Markov decision processes. In: Proceedings of the 21st international FLAIRS (The Florida Artificial Intelligence Research Society) conference, association for the advancement of artificial intelligence. Florida, USAFerguson D, Stentz A (2004) Focused propagation of MDPs for path planning. In: Proceedings of the 16th IEEE international conference on tools with artificial intelligence. pp 310–317Hansen EA, Zilberstein S (2001) LAO: a heuristic search algorithm that finds solutions with loops. Artif Intell 129: 35–62Hinderer K, Waldmann KH (2003) The critical discount factor for finite Markovian decision processes with an absorbing set. Math Methods Oper Res 57: 1–19Li L (2009) A unifying framework for computational reinforcement learning theory. PhD Thesis. The state university of New Jersey, New Brunswick. NJLittman ML, Dean TL, Kaelbling LP (1995) On the complexity of solving Markov decision problems.In: Proceedings of the 11th international conference on uncertainty in artificial intelligence. Montreal, Quebec pp 394–402McMahan HB, Gordon G (2005a) Fast exact planning in Markov decision processes. In: Proceedings of the 15th international conference on automated planning and scheduling. Monterey, CA, USAMcMahan HB, Gordon G (2005b) Generalizing Dijkstra’s algorithm and gaussian elimination for solving MDPs. Technical report, Carnegie Mellon University, PittsburghMeuleau N, Brafman R, Benazera E (2006) Stochastic over-subscription planning using hierarchies of MDPs. In: Proceedings of the 16th international conference on automated planning and scheduling. Cumbria, UK, pp 121–130Moore A, Atkeson C (1993) Prioritized sweeping: reinforcement learning with less data and less real time. Mach Learn 13: 103–130Puterman ML (1994) Markov decision processes. Wiley Editors, New YorkPuterman ML (2005) Markov decision processes. Wiley Inter Science Editors, New YorkRussell S (2005) Artificial intelligence: a modern approach. Making complex decisions (Ch-17), 2nd edn. Pearson Prentice Hill Ed., USAShani G, Brafman R, Shimony S (2008) Prioritizing point-based POMDP solvers. IEEE Trans Syst Man Cybern 38(6): 1592–1605Sniedovich M (2006) Dijkstra’s algorithm revisited: the dynamic programming connexion. Control Cybern 35: 599–620Sniedovich M (2010) Dynamic programming: foundations and principles, 2nd edn. Pure and Applied Mathematics Series, UKTijms HC (2003) A first course in stochastic models. Discrete-time Markov decision processes (Ch-6). Wiley Editors, UKVanderbei RJ (1996) Optimal sailing strategies. Statistics and operations research program, University of Princeton, USA ( http://www.orfe.princeton.edu/~rvdb/sail/sail.html )Vanderbei RJ (2008) Linear programming: foundations and extensions, 3rd edn. Springer, New YorkWingate D, Seppi KD (2005) Prioritization methods for accelerating MDP solvers. J Mach Learn Res 6: 851–88

A flexible coupling approach to multi-agent planning under incomplete information

The final publication is available at Springer via http://dx.doi.org/10.1007/s10115-012-0569-7Multi-agent planning (MAP) approaches are typically oriented at solving loosely coupled problems, being ineffective to deal with more complex, strongly related problems. In most cases, agents work under complete information, building complete knowledge bases. The present article introduces a general-purpose MAP framework designed to tackle problems of any coupling levels under incomplete information. Agents in our MAP model are partially unaware of the information managed by the rest of agents and share only the critical information that affects other agents, thus maintaining a distributed vision of the task. Agents solve MAP tasks through the adoption of an iterative refinement planning procedure that uses single-agent planning technology. In particular, agents will devise refinements through the partial-order planning paradigm, a flexible framework to build refinement plans leaving unsolved details that will be gradually completed by means of new refinements. Our proposal is supported with the implementation of a fully operative MAP system and we show various experiments when running our system over different types of MAP problems, from the most strongly related to the most loosely coupled.This work has been partly supported by the Spanish MICINN under projects Consolider Ingenio 2010 CSD2007-00022 and TIN2011-27652-C03-01, and the Valencian Prometeo project 2008/051.Torreño Lerma, A.; Onaindia De La Rivaherrera, E.; Sapena Vercher, O. (2014). A flexible coupling approach to multi-agent planning under incomplete information. Knowledge and Information Systems. 38:141-178. https://doi.org/10.1007/s10115-012-0569-7S14117838Argente E, Botti V, Carrascosa C, Giret A, Julian V, Rebollo M (2011) An abstract architecture for virtual organizations: the THOMAS approach. Knowl Inf Syst 29(2):379–403Barrett A, Weld DS (1994) Partial-order planning: evaluating possible efficiency gains. Artif Intell 67(1):71–112Belesiotis A, Rovatsos M, Rahwan I (2010) Agreeing on plans through iterated disputes. In: Proceedings of the 9th international conference on autonomous agents and multiagent systems. pp 765–772Bellifemine F, Poggi A, Rimassa G (2001) JADE: a FIPA2000 compliant agent development environment. In: Proceedings of the 5th international conference on autonomous agents (AAMAS). ACM, pp 216–217Blum A, Furst ML (1997) Fast planning through planning graph analysis. Artif Intell 90(1–2):281–300Boutilier C, Brafman R (2001) Partial-order planning with concurrent interacting actions. J Artif Intell Res 14(105):136Brafman R, Domshlak C (2008) From one to many: planning for loosely coupled multi-agent systems. In: Proceedings of the 18th international conference on automated planning and scheduling (ICAPS). pp 28–35Brenner M, Nebel B (2009) Continual planning and acting in dynamic multiagent environments. J Auton Agents Multiag Syst 19(3):297–331Coles A, Coles A, Fox M, Long D (2010) Forward-chaining partial-order planning. In: Proceedings of the 20th international conference on automated planning and scheduling (ICAPS). pp 42–49Coles A, Fox M, Long D, Smith A (2008) Teaching forward-chaining planning with JavaFF. In: Colloquium on AI education, 23rd AAAI conference on artificial intelligenceCox J, Durfee E, Bartold T (2005) A distributed framework for solving the multiagent plan coordination problem. In: Proceedings of the 4th international joint conference on autonomous agents and multiagent systems (AAMAS). ACM, pp 821–827de Weerdt M, Clement B (2009) Introduction to planning in multiagent systems. Multiag Grid Syst 5(4):345–355Decker K, Lesser VR (1992) Generalizing the partial global planning algorithm. Int J Coop Inf Syst 2(2):319–346desJardins M, Durfee E, Ortiz C, Wolverton M (1999) A survey of research in distributed continual planning. AI Mag 20(4):13–22Doshi P (2007) On the role of interactive epistemology in multiagent planning. In: Artificial intelligence and, pattern recognition. pp 208–213Dréo J, Savéant P, Schoenauer M, Vidal V (2011) Divide-and-evolve: the marriage of descartes and darwin. In: Proceedings of the 7th international planning competition (IPC). Freiburg, GermanyDurfee EH (2001) Distributed problem solving and planning. In: Multi-agents systems and applications: selected tutorial papers from the 9th ECCAI advanced course (ACAI) and agentLink’s third European agent systems summer school (EASSS), vol LNAI 2086. Springer, pp 118–149Durfee EH, Lesser V (1991) Partial global planning: a coordination framework for distributed hypothesis formation. IEEE Trans Syst Man Cybern Special Issue Distrib Sens Netw 21(5):1167–1183Ephrati E, Rosenschein JS (1996) Deriving consensus in multiagent systems. Artif Intell 87(1–2):21–74Fikes R, Nilsson N (1971) STRIPS: a new approach to the application of theorem proving to problem solving. Artif Intell 2(3):189–208Fogués R, Alberola J, Such J, Espinosa A, Garcia-Fornes A (2010) Towards dynamic agent interaction support in open multiagent systems. In: Proceedings of the 2010 conference on artificial intelligence research and development: proceedings of the 13th international conference of the Catalan association for artificial intelligence’. IOS Press, pp 89–98Gerevini A, Long D (2006) Preferences and soft constraints in PDDL3. In: ICAPS workshop on planning with preferences and soft constraints, vol 6. Citeseer, pp 46–53Ghallab M, Howe A, Knoblock C, McDermott D, Ram A, Veloso M, Weld D, Wilkins D (1998) PDDL-the Planning Domain Definition Language. In: AIPS-98 planning committeeGmytrasiewicz P, Doshi P (2005) A framework for sequential planning in multi-agent settings. J Artif Intell Res 24:49–79Haslum P, Jonsson P (1999) Some results on the complexity of planning with incomplete information. In: Proceedings of the 5th European conference on, planning (ECP). pp 308–318Helmert M (2006) The fast downward planning system. J Artif Intell Res 26(1):191–246Hoffmann J, Nebel B (2001) The FF planning system: fast planning generation through heuristic search. J Artif Intell Res 14:253–302Jonsson A, Rovatsos M (2011) Scaling up multiagent planning: a best-response approach. In: Proceedings of the 21st international conference on automated planning and scheduling (ICAPS). AAAI, pp 114–121Kambhampati S (1997) Refinement planning as a unifying framework for plan synthesis. AI Mag 18(2):67–97Kaminka GA, Pynadath DV, Tambe M (2002) Monitoring teams by overhearing: a multi-agent plan-recognition approach. J Artif Intell Res 17:83–135Kone M, Shimazu A, Nakajima T (2000) The state of the art in agent communication languages. Knowl Inf Syst 2(3):259–284Kovacs DL (2011) Complete BNF description of PDDL3.1. Technical reportKraus S (1997) Beliefs, time and incomplete information in multiple encounter negotiations among autonomous agents. Ann Math Artif Intell 20(1–4):111–159Kumar A, Zilberstein S, Toussaint M (2011) Scalable multiagent planning using probabilistic inference. In: Proceedings of the 22nd international joint conference on artificial intelligence (IJCAI)’. Barcelona, Spain, pp 2140–2146Kvarnström J. (2011) Planning for loosely coupled agents using partial order forward-chaining. In: Proceedings of the 21st international conference on automated planning and scheduling (ICAPS). AAAI, pp 138–145Lesser V, Decker K, Wagner T, Carver N, Garvey A, Horling B, Neiman D, Podorozhny R, Prasad M, Raja A et al (2004) Evolution of the GPGP/TAEMS domain-independent coordination framework. Auton Agents Multi Agent Syst 9(1):87–143Lipovetzky N, Geffner H (2011) Searching for plans with carefully designed probes. In: Proceedings of the 21th international conference on automated planning and scheduling (ICAPS)Micacchi C, Cohen R (2008) A framework for simulating real-time multi-agent systems. Knowl Inf Syst 17(2):135–166Nguyen N, Katarzyniak R (2009) Actions and social interactions in multi-agent systems. Knowl Inf Syst 18(2):133–136Nguyen X, Kambhampati S (2001) Reviving partial order planning. In: Proceedings of the 17th international joint conference on artificial intelligence (IJCAI). Morgan Kaufmann, pp 459–464Nissim R, Brafman R, Domshlak C (2010) A general, fully distributed multi-agent planning algorithm. In: Proceedings of the 9th international conference on autonomous agents and multiagent systems (AAMAS). pp 1323–1330Pajares S, Onaindia E (2012) Defeasible argumentation for multi-agent planning in ambient intelligence applications. In: Proceedings of the 11th international conference on autonomous agents and multiagent systems (AAMAS) pp 509–516Paolucci M, Shehory O, Sycara K, Kalp D, Pannu A (2000) A planning component for RETSINA agents. Intelligent Agents VI. Agent Theories Architectures, and Languages pp 147–161Parsons S, Sierra C, Jennings N (1998) Agents that reason and negotiate by arguing. J Logic Comput 8(3):261Penberthy J, Weld D (1992) UCPOP: a sound, complete, partial order planner for ADL. In: Proceedings of the 3rd international conference on principles of knowledge representation and reasoning (KR). Morgan Kaufmann, pp 103–114Richter S, Westphal M (2010) The LAMA planner: guiding cost-based anytime planning with landmarks. J Artif Intell Res 39(1):127–177Sycara K, Pannu A (1998) The RETSINA multiagent system (video session): towards integrating planning, execution and information gathering. In: Proceedings of the 2nd international conference on autonomous agents (Agents). ACM, pp 350–351Tambe M (1997) Towards flexible teamwork. J Artif Intell Res 7:83–124Tang Y, Norman T, Parsons S (2010) A model for integrating dialogue and the execution of joint plans. Argumentation in multi-agent systems, pp 60–78Tonino H, Bos A, de Weerdt M, Witteveen C (2002) Plan coordination by revision in collective agent based systems. Artif Intell 142(2):121–145Van Der Krogt R, De Weerdt M (2005), Plan repair as an extension of planning. In: Proceedings of the 15th international conference on automated planning and scheduling (ICAPS). pp 161–170Weld D (1994) An introduction to least commitment planning. AI Mag 15(4):27Weld D (1999) Recent advances in AI planning. AI Mag 20(2):93–123Wilkins D, Myers K (1998) A multiagent planning architecture. In: Proceedings of the 4th international conference on artificial intelligence planning systems (AIPS), pp 154–162Wu F, Zilberstein S, Chen X (2011) Online planning for multi-agent systems with bounded communication. Artif Intell 175(2):487–511Younes H, Simmons R (2003) VHPOP: versatile heuristic partial order planner. J Artif Intell Res 20: 405–430Zhang J, Nguyen X, Kowalczyk R (2007) Graph-based multi-agent replanning algorithm. In: Proceedings of the 6th conference on autonomous agents and multiagent systems (AAMAS

Sustainable Development, Ecological Complexity, and Environmental Values

Sustainable Development, Ecological Complexity, and Environmental Values contributes to expanding the idea of sustainability by integrating different thematic issues related to sustainable development in its threefold consideration (economic, social, and environmental) with regard to the case of the Basque Country. On the global scale, changes have clearly accelerated; ecological and social sustainability are two facets of the same changing reality. First, social sustainability depends on ecological sustainability. If we continue degrading nature's capacity to produce the ecosystems' services (water filtration, climate stabilization, etc) and resources (food, materials), both individuals and nations will be affected by growing pressures and increasing conflicts, as well as by threats to public health and personal safety. Second, ecological sustainability depends on social sustainability, a socially unjust and unfair system wiht an ever-increasing population that is not able to have its needs met will necessarily lead to environmental collapse. In addition, human behavior and the social dynamic often lie at the heart of social and ecological problems. It must be, therefore, assumed that there will not be sustainable development if sustainable societies do not first exist. A sustainable society has the challenge of developing human capital. In this book, these global questions are treated as they relate to specific place and context, the Basque Country and its modern institutions.This book was published with generous financial support from the Basque Government.Introduction—Ignacio Ayestarán and Miren Onaindia ? 1. An Evaluation of Ecosystem Services as a Base for the Sustainable Management of a Region by Miren Onaindia and Gloria Rodríguez-Loinaz ? 2. An Evaluation of Millennium Ecosystems from the Basque Country by Igone Palacios, Izaskun Casada-Arzuaga, Iosu Madariaga, and Xabier Arana ? 3. Climate Change: Activities of the EOLO Group at the University of the Basque Country by Agustín Ezcurra, Jon Sáenz, and Gabriel Ibarra-Berastegi ? 4. The Environmental Value of the Karstic Landscape of the Urdaibai Biosphere Reserve: The Asnarre Promontory (Bizkaia) by Arantza Aranburu, Laura Damas-Mollá, Patxi García-Garmilla, Iñaki Yusta, M. Arriolabengoa, Peru Iridoy, and Eneko Iriarte ? 5. Recent Environmental Transformation of the Bilbao Estuary: Natural and Anthropogenic Processes by Alejandro Cearreta, Maria Jesús Irabien, and Eduardo Leorri ? 6. The Landscape of the Autonomous Community of the Basque Country: The Evolution of Forest Systems by Lorena Peña and Ibone Amezaga ? 7. Critical Theories of Sustainable Development by Eguzki Urteaga ? 8. Bases for the Transition toward a Sustainable Economy by Roberto Bermejo, David Hoyos, and Eneko Garmendia ? 9. Environmental Values, the Epistemology of Complex Problems, and Postnormal Science in the Face of Global Change by Ignacio Ayestáran ? 10. Science, Gender, and Sustainable Development by Teresa Nuño Angós ? 11. Environmental Education as Training: A Case Study at the University of the Basque Country by Araitz Uskola Ibarluzea ? 12. Social Values and Sustainable Practices among Basque Inshore Fishermen by Pío Pérez Aldasoro ? 13. Sustainable Development and the Values of Well-Being and Globalization by Eduardo Rubio Ardanaz, Juan Antonio Rubio-Ardanaz, and Xiao Fang ? Index ? List of Contributor

Mapping and Assessment of forest Ecosystem and Their Services. Applications and guidance for decision making in the framework of MAES

The aim of this report is to illustrate by means of a series of case studies the implementation of mapping and assessment of forest ecosystem services in different contexts and geographical levels. Methodological aspects, data issues, approaches, limitations, gaps and further steps for improvement are analysed for providing good practices and decision making guidance. The EU initiative on Mapping and Assessment of Ecosystems and their Services (MAES), with the support of all Member States, contributes to improve the knowledge on ecosytem services. MAES is one of the building-block initiatives supporting the EU Biodiversity Strategy to 2000

Molecular basis of targeted therapy in T/NKcell lymphoma/leukemia: A comprehensive genomic and immunohistochemical analysis of a panel of 33 cell lines

T and NK-cell lymphoma is a collection of aggressive disorders with unfavorable outcome, in which targeted treatments are still at a preliminary phase. To gain deeper insights into the deregulated mechanisms promoting this disease, we searched a panel of 31 representative T-cell and 2 NK-cell lymphoma/leukemia cell lines for predictive markers of response to targeted therapy. To this end, targeted sequencing was performed alongside the expression of specific biomarkers corresponding to potentially activated survival pathways. The study identified TP53, NOTCH1 and DNMT3A as the most frequently mutated genes. We also found common alterations in JAK/STAT and epigenetic pathways. Immunohistochemical analysis showed nuclear accumulation of MYC (in 85% of the cases), NFKB (62%), p-STAT (44%) and p-MAPK (30%). This panel of cell lines captures the complexity of T/NK-cell lymphoproliferative processes samples, with the partial exception of AITL cases. Integrated mutational and immunohistochemical analysis shows that mutational changes cannot fully explain the activation of key survival pathways and the resulting phenotypes. The combined integration of mutational/expression changes forms a useful tool with which new compounds may be assayed