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

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

Automated Analysis in Feature Modelling and Product Configuration

The automated analysis of feature models is one of the thriving topics of research in the software product line and variability management communities that has attracted more attention in the last years. A recent literature review reported that more than 30 analysis operations have been identi ed and di erent analysis mechanisms have been proposed. Product con guration is a well established research eld with more than 30 years of successful applications in di erent industrial domains. Our hypothesis, that is not really new, is that these two independent areas of research have interesting synergies that have not been fully explored. To try to explore the potential synergies systematically, in this paper we provide a rapid review to bring together these previously disparate streams of work. We de ne a set of research questions and give a preliminary answer to some of them. We conclude that there are many research opportunities in the synergy of these independent areas.Ministerio de Ciencia e Innovación TIN2009- 07366Junta de Andalucía TIC-590

Leveraging analytics to produce compelling and profitable film content

Producing compelling film content profitably is a top priority to the long-term prosperity of the film industry. Advances in digital technologies, increasing availabilities of granular big data, rapid diffusion of analytic techniques, and intensified competition from user generated content and original content produced by Subscription Video on Demand (SVOD) platforms have created unparalleled needs and opportunities for film producers to leverage analytics in content production. Built upon the theories of value creation and film production, this article proposes a conceptual framework of key analytic techniques that film producers may engage throughout the production process, such as script analytics, talent analytics, and audience analytics. The article further synthesizes the state-of-the-art research on and applications of these analytics, discuss the prospect of leveraging analytics in film production, and suggest fruitful avenues for future research with important managerial implications