Experiences on Using Intelligent Planning for Curriculum Personalization in Moodle

In this paper we discuss our experiences on using artificial intelligence to plan customized learning paths in the Moodle platform. In particular, we found some limitations in defining students’ profiles, complex relationships between activities and personalized views of the learning contents. We show how we solved this kind of problems in order to create an integrated system for the application of our planning approach in MoodleValentina Caputi thanks the European Commission, the European Social Fund, and the Regione Calabria for financial support of her PhD fellowship and funding for her stay in Valencia. This paper is co-funded with support from the European Commission, the European Social Fund and the Regione Calabria. The paper was also partially funded by the Consolider AT project CSD2007- 0022 INGENIO 2010 of the Spanish Ministry of Science and Innovation, the MICINN project TIN2011- 27652-C03-01 and the Valencian Prometeo project 2008/051. The European Commission and the Regione Calabria disclaim any responsibility for the use that may be made of the information contained in this publicationCaputi, V.; Garrido Tejero, A. (2013). Experiences on Using Intelligent Planning for Curriculum Personalization in Moodle. En EDULEARN13 Proceedings. IATED. 168-176. http://hdl.handle.net/10251/71817S16817

On the Necessity of Time and Resource Issues to Support Collaboration in E-learning Standards

In this paper we motivate the necessity of time+resource metadata in current e-learning standards to support collaborative activities. Learning Objects (LOs) are currently defined in a very independent way from each other, which makes it difficult to use them in a real scenario where students interact and have their own constraints. We present some challenging features that, at least, should be discussed when elaborating new e-learning standards.Garrido Tejero, A.; Morales, L.; Serina, I. (2011). On the Necessity of Time and Resource Issues to Support Collaboration in E-learning Standards. IEEE Learning Technology Newsletter. 13:39-41. http://hdl.handle.net/10251/35041S39411

mWater prototype review

This document reviews our current water policy-making decision-support framework, build on top of a regulated open Multi-Agent System (MAS),mWater [BGG+10, GGG+11], that models a flexible water-rights market. Our simulator focuses on the effect of regulations on demand and thus provides means to explore the interplay of norms and conventions that regulate trading (like trader eligibility conditions, tradeable features of rights, trading periods and price-fixing conventions), the assumptions about agent behaviour (individual preferences and risk attitude, or population profile mixtures) and market scenarios (water availability and use restrictions). A policy-maker would then assess the effects of those interactions by observing the evolution of the performance indicators (efficiency of use, price dynamics, welfare functions) (s)he designs. 1.2 OurBotti Navarro, VJ.; Garrido Tejero, A.; Giret Boggino, AS.; Noriega, P. (2013). mWater prototype review. http://hdl.handle.net/10251/3212

A MAS-based infrastructure for negotiation and its application to a water-right market

The final publication is available at Springer via http://dx.doi.org/10.1007/s10796-013-9443-8This paper presents a MAS-based infrastructure for the specification of a negotiation framework that handles multiple negotiation protocols in a coherent and flexible way. Although it may be used to implement one single type of agreement mechanism, it has been designed in such a way that multiple mechanisms may be available at any given time, to be activated and tailored on demand (on-line) by participating agents. The framework is also generic enough so that new protocols may be easily added. This infrastructure has been successfully used in a case study to implement a simulation tool as a component of a larger framework based on an electronic market of water rights.This paper was partially funded by the Consolider AT project CSD2007-0022 INGENIO 2010 of the Spanish Ministry of Science and Innovation; the MICINN projects TIN2011-27652-C03-01 and TIN2009-13839-C03-01; and the Valencian Prometeo project 2008/051.Alfonso Espinosa, B.; Botti Navarro, VJ.; Garrido Tejero, A.; Giret Boggino, AS. (2014). A MAS-based infrastructure for negotiation and its application to a water-right market. Information Systems Frontiers. 16(2):183-199. https://doi.org/10.1007/s10796-013-9443-8S183199162Alberola, J.M., Such, J.M., Espinosa, A., Botti, V., García-Fornes, A. (2008). Magentix: a multiagent platform integrated in linux. In EUMAS (pp. 1–10).Alfonso, B., Vivancos, E., Botti, V., García-Fornes, A. (2011). Integrating jason in a multi-agent platform with support for interaction protocols. In Proceedings of the compilation of the co-located workshops on AGERE!’11, SPLASH ’11 workshop (pp. 221–226). New York: ACM.Andreu, J., Capilla, J., Sanchis, E. (1996). AQUATOOL, a generalized decision-support system for water-resources planning and operational management. Journal of Hydrology, 177(3–4), 269–291.Bellifemine, F., Caire, G., Greenwood, D. (2007). Developing multi-agent systems with JADE. Wiley.Bordini, R.H., Hübner, J.F., Wooldridge, M. (2007). Programming multi-agent systems in agent speak usign Jason. Wiley.Botti, V., Garrido, A., Gimeno, J.A., Giret, A., Noriega, P. (2011). The role of MAS as a decision support tool in a water-rights market. In AAMAS 2011 workshops, LNAI 7068 (pp. 35–49). Springer.Braubach, L., Pokahr, A., Lamersdorf, W. (2005). Software agent-based applications, platforms and development kits In C.M.K.R. Unland (Ed.), Jadex: a BDI agent system combining middleware and reasoning (Vol. 9, pp. 143–168): Birkhäuser-Verlag.DeSanctis, G.B., & Gallupe, B. (1987). A foundation for the study of group decision support systems. Knowledge based systems, 33(5), 589–609.Eckersley, P. (2003). Virtual markets for virtual goods. Available at http://www.ipria.com/publications/wp/2003/IPRIAWP02.2003.pdf (Accessed April 2012).Fjermestad, J., & Hiltz, S. (2001). Group support systems: a descriptive evaluation of case and field studies. Journal of Management Information Systems, 17(3), 115–161.Fogués, R.L., Alberola, J.M., Such, J.M., Espinosa, A., García-Fornes, A. (2010). Towards dynamic agent interaction support in open multiagent systems. In Proceedings of the 13th international conference of the catalan association for artificial intelligence (Vol. 220, pp. 89–98). IOS Press.Foundation for Intelligent Physical Agents. (2001). FIPA interaction protocol library specification XC00025E. FIPA Consortium.Garrido, A., Arangu, M., Onaindia, E. (2009). A constraint programming formulation for planning: from plan scheduling to plan generatio. Journal of Scheduling, 12(3), 227–256.Giret, A., Garrido, A., Gimeno, J.A., Botti, V., Noriega, P. (2011). A MAS decision support tool for water-right markets. In Proceedings of the tenth international conference on autonomous agents and multiagent systems (Demonstrations@AAMAS) (pp. 1305–1306).Gomez-Limon, J., & Martinez, Y. (2006). Multi-criteria modelling of irrigation water market at basin level: a Spanish case study. European Journal of Operational Research, 173, 313–336.Janjua, N.K., Hussain, F.K., Hussain, O.K. (2013). Semantic information and knowledge integration through argumentative reasoning to support intelligent decision making. Information Systems Frontiers, 15(2), 167–192.jen Hsu, J.Y., Lin, K.-J., Chang, T.-H., ju Ho, C., Huang, H.-S., rong Jih, W. (2006). Parameter learning of personalized trust models in broker-based distributed trust management. Information Systems Frontiers, 8(4), 321–333.Kersten, G., & Lai, H. (2007). European Journal of Operational Research, 180(2), 922–937.Lee, N., Bae, J.K., Koo, C. (2012). A case-based reasoning based multi-agent cognitive map inference mechanism: an application to sales opportunity assessment. Information Systems Frontiers, 14(3), 653–668.Luck, M., & AgentLink. (2005). Agent technology: computing as interaction: a roadmap for agent-based computing. Compiled, written and edited by Michael Luck et al. AgentLink, Southampton.Ma, J., & Orgun, M.A. (2008). Formalizing theories of trust for authentication protocols. Information Systems Frontiers, 10(1), 19–32.Pokahr, A., Braubach, L., Walczak, A., Lamersdorf, W. (2007). Developing multi-agent systems with JADE. Jadex-Engineering Goal-Oriented Agents (pp. 254258). Wiley.Ramos, C., Cordeiro, M., Praça, I., Vale, Z. (2005). Intelligent agents for negotiation and game-based decision support in electricity market. Engineering Intelligent Systems for Electrical Engineering and Communications, 13(2), 147–154.Sierra, C., Botti, V., Ossowski, S. (2011). Agreement computing. KI - Künstliche Intelligenz, 25(1), 57–61.Thobani, M. (1997). Formal water markets: why, when and how to introduce tradable water rights. The World Bank Research Observer, 12(2), 161–179

mWater prototype #3 analysis and design

In themWater case study prototype #3 it has been used Magentix2 [1, 24, 3, 22, 4, 17] (for more details on Magentix2 see WP7 Deliverables) as the MAS platform for supporting the execution of the MAS system. The platform follows the FIPA standards [14] offering a set of useful mechanisms for the agents to communicate and also tools to allow programming agents in a high level language based on the BDI model. Magentix2 is an open system which facilitates the interaction between heterogeneous agents through FIPA-ACL messages. Also complex interactions can be carried out in a flexible an open way as conversations. The platform offers special structures to allow to use such conversations by considering a set of issues: In each conversation there are always two roles involved: Initiator and Participant. The first is the one who initiates the conversation, and the rest of agents play the Participant role. The conversation can be seen as a direct graph where nodes represent the actions to perform in each step of the conversation and arcs represent the transition between such states. Those steps allow to perform some actions and they can be of different kinds, for example: Begin, Final, Wait, Send, Receive, Action, etc. Conversations have a unique identifier that allows to manage them individually. 1Botti Navarro, VJ.; Garrido Tejero, A.; Giret Boggino, AS.; Noriega, P.; Bexi, A. (2013). mWater prototype #3 analysis and design. http://hdl.handle.net/10251/3212

mWater Prototype 3

This report concerns the application of a regulated open Multi-Agent System (MAS), mWater, that uses intelligent agents to simulate a flexible water-right market. Our simulator focuses on demands and, in particular, on the type of regulatory (in terms of norms selection and agents behaviour), and market mechanisms that foster an efficient use of water while also trying to prevent conflicts among parties. In this scenario, a MAS plays a vital role as it allows us to define different norms, agents behaviour and roles, and assess their impact in the market, thus enhancing the quality and applicability of its results as a decision support tool.Botti Navarro, VJ.; Garrido Tejero, A.; Giret Boggino, AS.; Noriega, P.; Gimeno, J. (2013). mWater Prototype 3. http://hdl.handle.net/10251/3212

mWater prototype #3 review

mWater is a software demonstrator developed in the Agreement Technologies Project. It is a Multi-Agent System (MAS) application that implements a market for water rights, including the model and simulation of the water-right market itself, the basin, users, protocols, norms and grievance situations. mWater is motivated due to the fact that water scarcity is becoming a major concern in most countries, not only because it threatens the economic viability of current agricultural practices, but because it is likely to alter an already precarious balance among its different types of use.Garrido Tejero, A.; Botti Navarro, VJ.; Giret Boggino, AS.; Alfonso Espinosa, B.; Noriega, P. (2013). mWater prototype #3 review. http://hdl.handle.net/10251/3181

mWater Analysis

The mWater scenario requires the expression and use of regulations of different sorts: from actual laws and regulations issued by governments, to policies and local regulations issued by basin managers, to social norms that prevail in a given community of users. Some will be regimented as part of the electronic institutional framework specification, but others need to be expressed in a declarative form so that one may reason about them, both off- and on-line, both at design and at run time, and both from the institutional (or legislative) perspective and the agent's individual perspective. Issues that are relevant in this respect range from the choice of expressive formalism to the decision-making strategies that agents might use to comply or disobey regulations. Thus, structural aspects like governance, dynamics of norms (also from the legislative and execution perspectives) as well as criteria to evaluate the effectiveness of norms may and need to be explored in the demonstrator.Botti Navarro, VJ.; Garrido Tejero, A.; Giret Boggino, AS.; Igual, F.; Noriega, P.; Igual (2013). mWater Analysis. http://hdl.handle.net/10251/3210

On the automatic compilation of e-learning models to planning

[EN] This paper presents a general approach to automatically compile e-learning models to planning, allowing us to easily generate plans, in the form of learning designs, by using existing domain-independent planners. The idea is to compile, first, a course defined in a standard e-learning language into a planning domain, and, second, a file containing students learning information into a planning problem. We provide a common compilation and extend it to three particular approaches that cover a full spectrum of planning paradigms, which increases the possibilities of using current planners: (i) hierarchical, (ii) including PDDL (Planning Domain Definition Language) actions with conditional effects and (iii) including PDDL durative actions. The learning designs are automatically generated from the plans and can be uploaded, and subsequently executed, by learning management platforms. We also provide an extensive analysis of the e-learning metadata specification required for planning, and the pros and cons on the knowledge engineering procedures used in each of the three compilations. Finally, we include some qualitative and quantitative experimentation of the compilations in several domain-independent planners to measure its scalability and applicability.This work has been supported by the Spanish MICINN under projects TIN2008-06701-C03 and Consolider Ingenio 2010 CSD2007-00022, by the Mexican National Council of Science and Technology and the regional projects CCG08-UC3M/TIC-4141 and Prometeo GVA 2008/051.Garrido Tejero, A.; Fernandez, S.; Onaindia De La Rivaherrera, E.; Morales, L.; Borrajo, D.; Castillo, L. (2013). On the automatic compilation of e-learning models to planning. Knowledge Engineering Review. 28(2):121-136. https://doi.org/10.1017/S0269888912000380S121136282Garrido A. , Onaindía E. 2010. On the application of planning and scheduling techniques to E-learning. In Proceedings of the 23rd International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA-AIE 2010)—Lecture Notes in Computer Science 6096, 244–253. Springer.Ullrich C 2008. Pedagogically founded courseware generation for web-based learning, No. 5260, Lecture Notes in Artificial Intelligence 5260, Springer.Sicilia M.A. , Sánchez-Alonso S. , García-Barriocanal E. 2006. On supporting the process of learning design through planners. CEUR Workshop Proceedings: Virtual Campus 2006 Post-Proceedings. Barcelona, Spain, 186(1), 81–89.IMSLD 2003. IMS Learning Design Specification. Version 1.0 (February, 2003). Retrieved December, 2012, from http://www.imsglobal.org/learningdesign.Sharable Content Object Reference Model (SCORM) 2004. Retrieved December, 2012, from http://scorm.com.Garrido A. , Onaindia E. , Morales L. , Castillo L. , Fernandez S. , Borrajo D. 2009. Modeling E-learning activities in automated planning. In Proceedings of the 3rd International Competition on Knowledge Engineering for Planning and Scheduling (ICKEPS-2009), Thessaloniki, Greece, 18–27.Essalmi, F., Ayed, L. J. B., Jemni, M., Kinshuk, & Graf, S. (2010). A fully personalization strategy of E-learning scenarios. Computers in Human Behavior, 26(4), 581-591. doi:10.1016/j.chb.2009.12.010Camacho D. , R-Moreno M.D. , Obieta U. 2007. CAMOU: a simple integrated e-learning and planning techniques tool. In 4th International Workshop on Constraints and Language Processing, Roskilde University, Denmark, 1–11.Fox, M., & Long, D. (2003). PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains. Journal of Artificial Intelligence Research, 20, 61-124. doi:10.1613/jair.1129KONTOPOULOS, E., VRAKAS, D., KOKKORAS, F., BASSILIADES, N., & VLAHAVAS, I. (2008). An ontology-based planning system for e-course generation. Expert Systems with Applications, 35(1-2), 398-406. doi:10.1016/j.eswa.2007.07.034Fuentetaja R. , Borrajo D. , Linares López C. 2009. A look-ahead B&B search for cost-based planning. In Proceedings of CAEPIA'09, Murcia, Spain, 105–114.Limongelli C. , Sciarrone F. , Vaste G. 2008. LS-plan: an effective combination of dynamic courseware generation and learning styles in web-based education. In Adaptive Hypermedia and Adaptive Web-Based Systems, 5th International Conference, AH 2008, Nejdl, W., Kay, J., Pu, P. & Herder, E. (eds.)., 133–142. Springer.Castillo L. , Fdez.-Olivares J. , García-Perez O. Palao F. 2006. Efficiently handling temporal knowledge in an HTN planner. In Proceedings of 16th International Conference on Automated Planning and Scheduling (ICAPS 2006), Borrajo, D. & McCluskey, L. (eds.). AAAI, 63–72.Castillo, L., Morales, L., González-Ferrer, A., Fdez-Olivares, J., Borrajo, D., & Onaindía, E. (2009). Automatic generation of temporal planning domains for e-learning problems. Journal of Scheduling, 13(4), 347-362. doi:10.1007/s10951-009-0140-xUllrich, C., & Melis, E. (2009). Pedagogically founded courseware generation based on HTN-planning. Expert Systems with Applications, 36(5), 9319-9332. doi:10.1016/j.eswa.2008.12.043Boticario J. , Santos O. 2007. A dynamic assistance approach to support the development and modelling of adaptive learning scenarion based on educational standards. In Proceedings of Workshop on Authoring of Adaptive and Adaptable Hypermedia, International Conference on User Modelling, Corfu, Greece, 1–8.IMSMD 2003. IMS Learning Resource Meta-data Specification. Version 1.3 (August, 2006). Retrieved December, 2012, from http://www.imsglobal.org/metadata.Mohan P. , Greer J. , McCalla G. 2003. Instructional planning with learning objects. In IJCAI-03 Workshop Knowledge Representation and Automated Reasoning for E-Learning Systems, Acapulco, Mexico, 52–58.Alonso C. , Honey P. 2002. Honey-alonso Learning Style Theoretical Basis (in Spanish). Retrieved December 2012, from http://www.estilosdeaprendizaje.es/menuprinc2.htm

mWater prototype #2 analysis and design

mWater is a regulated open MAS that uses intelligent agents to manage a flexible water-right market. One of the main goals of mWater is to be used as a simulator to assist in decision-taking processes for policy makers. Our simulator focuses on demands and, in particular, on the type of regulatory (in terms of norms selection and agents behaviour), and market mechanisms thatBotti Navarro, VJ.; Criado Pacheco, N.; Garrido Tejero, A.; Gimeno, J.; Giret Boggino, AS.; Noriega, P. (2013). mWater prototype #2 analysis and design. http://hdl.handle.net/10251/3212