Abstract
Today, handheld devices can accommodate a large amount of different resources. Thus, a considerable effort is often required to mobile users in order to search for the resources suitable for the specific circumstance. Further, this effort rarely brings to a satisfactory result. To ease this work, resource recommenders have been proposed in the last years. Typically, the recommendation is based on recognizing the current situations of the users and suggesting them the appropriate resources for those situations. The recognition task is performed by exploiting contextual information and preferably without using any explicit input from the user. To this aim, we propose to adopt a collaborative scheme based on an emergent paradigm. The underlying idea is that simple individual actions can lead to an emergent collective behavior that represents an implicit form of contextual information. We show how this behavior can be extracted by using a multi-agent scheme, where agents do not directly communicate amongst themselves, but rather through the environment. The multi-agent scheme is structured into three levels of information processing. The first level is based on a stigmergic paradigm, in which marking agents leave marks in the environment in correspondence to the position of the user. The accumulation of such marks enables the second level, a fuzzy information granulation process, in which relevant events can emerge and are captured by means of event agents. Finally, in the third level, a fuzzy inference process, managed by situation agents, deduces the user situations from the underlying events. The proposed scheme is evaluated on a set of representative real scenarios related to meeting events. In all the scenarios, the collaborative situation-aware scheme promptly recognizes the correct situations, except for one case, thus proving its effectiveness.
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Adomavicius G, Tuzhilin A (2010) Context-aware recommender systems. In: Ricci F, Rokach L, Shapira B, Kantor PB (eds) Recommender systems handbook. Springer, US, pp 217–253
Arabo A, Shi Q, Merabti M (2009) A framework for user-centred and context-aware identity management in mobile ad hoc networks (UCIM). Ubiquitous Comput Commun J Spec Issue New Technol Mobil Secur 1–11
Arabo A, Shi Q, Merabti M (2011) ContextRank: begetting order to usage of context information and identity management in pervasive ad-hoc environments. In J. Symonds (ed), Emerging pervasive and ubiquitous aspects of information systems: cross-disciplinary advancements IGI global, pp 275–297. doi: 10.4018/978-1-60960-487-5.ch016
Bargiela A, Pedrycz W (2003) Granular computing. An introduction. Kluwer Academic Publishers, USA
Barron P (2005) Using stigmergy to build pervasive computing environments. PhD Thesis in Computer Science, University of Dublin, Trinity College
Brueckner SA, Van Dyke Parunak H (2005) Swarming distributed pattern detection and classification. In: Weyns D et al. (eds) Proceedings of the first international workshop on environments for multi-agent systems (E4MAS 2004), Lecture notes in artificial intelligence 3374, Springer-Verlag, Berlin Heidelberg, pp 232–245
Burke R (2002) Hybrid recommender systems: survey and experiments. User Model User-Adap Inter 12(4):331–370. doi:10.1023/A:1021240730564
Cena F, Console L, Gena C, Goy A, Levi G, Modeo S, Torre I (2006) Integrating heterogeneous adaptation techniques to build a flexible and usable mobile tourist guide. AI Communications 19(4):369–384
Ciaramella A, Cimino MGCA, Lazzerini B, Marcelloni F (2010a) A situation-aware resource recommender based on fuzzy and semantic web rules. Inter J Uncertain Fuzziness Knowl-Based Syst 18(4):411–430. doi:10.1142/S0218488510006623
Ciaramella A, Cimino MGCA, Lazzerini B, Marcelloni F (2010b) Using context history to personalize a resource recommender via a genetic algorithm. In: Proceedings of IEEE international conference on intelligent systems design and applications (ISDA’10). Cairo, pp 965–970
Cimino MGCA, Marcelloni F (2011) Autonomic tracing of production processes with mobile and agent-based computing. Inf Sci 181(5):935–953. doi:10.1016/j.ins.2010.11.015
Cimino MGCA, Lazzerini B, Marcelloni F, Ciaramella A (2012) An adaptive rule-based approach for managing situation-awareness, expert systems with applications, Elsevier Science. doi: 10.1016/j.eswa.2012.03.014 (in press)
De Maio C, Fenza G, Gaeta M, Loia V, Orciuoli F (2011) A knowledge-based framework for emergency DSS. Knowl-Based Syst 24(8):1372–1379. doi:10.1016/j.knosys.2011.06.011
Herlocker JL, Konstan JA (2001) Content-independent task-focused recommendation. IEEE Internet Comput 5(6):40–47. doi:10.1109/4236.968830
Heylighen F, Gershenson C (2003) The meaning of self-organization in computing. IEEE Intell Syst Sect Trends Controv Self-Organ Inf Syst 18(4):72–75
Holland O, Melhuish C (1999) Stigmergy, self-organization, and sorting in collective robotics. Artif Life 5(2):173–202. doi:10.1162/106454699568737
Jiang K, Wang P, Yu N (2011) ContextRank: personalized tourism recommendation by exploiting context information of geotagged web photos. In: Proceeding of sixth international conference on image and graphics (ICIG). Hefei, Anhui, pp 931–937
Luther M, Fukazawa Y, Wagner M, Kurakake A (2008) Situational reasoning for task-oriented mobile service recommendation. Knowl Eng Rev 23:7–19. doi:10.1017/S0269888907001300
Margaliot M (2008) Biomimicry and fuzzy modeling: a match made in heaven. IEEE Comput Intell Mag 3:28–48. doi:10.1109/MCI.2008.926602
Naganuma T, Kurakake S (2005) A task oriented approach to service retrieval in mobile computing environment. In: Proceedings of IASTED international conference on artificial intelligence and applications, Innsbruck (14-16/2/2005), pp 527–532
Park HS, Yoo JO, Cho SB (2006) A context-aware music recommendation system using fuzzy bayesian networks with utility theory. In: Wang L, Jiao L, Shi G, Li X, Liu J (eds) Fuzzy systems and knowledge discovery. Lecture notes in computer science. Springer, Heidelberg, pp 970–979
Petry H, Tedesco P, Vieira V, Salgado AC (2008) ICARE: A context-sensitive expert recommendation system. In: Proceedings of workshop on recommender systems (ECAI’08), Patras, pp 53–58
Rao VS (2010) Multi-agent distributed data mining: an overview. Inter J Rev Comput 3(30):83–92
Resnick P, Iacovou N, Suchak M, Bergstrom P, Riedl J (1994) Grouplens: an open architecture for collaborative filtering on etnews. In: Proceedings of the 1994 ACM conference on computer supported collaborative work, Chapel Hill, pp 175–186
Ricci F (2011) Mobile recommender systems. Inter J Inf Technol Tour 12(3):205–231. doi:10.3727/109830511X12978702284390
Rozin V, Margaliot M (2007) The fuzzy ant. IEEE Comput Intell Mag 2:18–28. doi:10.1109/FUZZY.2006.1681932
Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international world wide web conference, Hong Kong, pp 285–295
Schockaert S, De Cock M, Cornelis C, Kerre EE (2004) Fuzzy ant based clustering. In: Dorigo M, Birattari M, Blum C, Gambardella LM, Mondada F, Stutzle T (eds) Ant colony, optimization and swarm intelligence. Lecture notes in computer science. Springer, Heidelberg, pp 11–17
Sipper M (2002) Machine Nature: the coming age of bio-inspired computing. McGraw-Hill, New York
Terveen L, Hill W (2001) Beyond recommender systems: helping people help each other. In: Carroll JM (ed) Human–computer interaction in the new millennium. Addison Wesley, New York, pp 487–509
Vernon D, Metta G, Sandini G (2007) A survey of artificial cognitive systems: implications for the autonomous development of mental capabilities in computational agents. IEEE Trans Evol Comput 11(2):151–180. doi:10.1109/TEVC.2006.890274
Weißenberg N (2006) An ontology-based approach to personalized situation-aware mobile service supply. GeoInformatica 10(1):55–90. doi:10.1007/s10707-005-4886-9
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Castellano, G., Cimino, M.G.C.A., Fanelli, A.M. et al. A collaborative situation-aware scheme based on an emergent paradigm for mobile resource recommenders. J Ambient Intell Human Comput 4, 421–437 (2013). https://doi.org/10.1007/s12652-012-0126-y
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DOI: https://doi.org/10.1007/s12652-012-0126-y