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

The 2014 International Planning Competition: Progress and Trends

We review the 2014 International Planning Competition (IPC-2014), the eighth in a series of competitions starting in 1998. IPC-2014 was held in three separate parts to assess state-of-the-art in three prominent areas of planning research: the deterministic (classical) part (IPCD), the learning part (IPCL), and the probabilistic part (IPPC). Each part evaluated planning systems in ways that pushed the edge of existing planner performance by introducing new challenges, novel tasks, or both. The competition surpassed again the number of competitors than its predecessor, highlighting the competition’s central role in shaping the landscape of ongoing developments in evaluating planning systems

Engineering a Conformant Probabilistic Planner

We present a partial-order, conformant, probabilistic planner, Probapop which competed in the blind track of the Probabilistic Planning Competition in IPC-4. We explain how we adapt distance based heuristics for use with probabilistic domains. Probapop also incorporates heuristics based on probability of success. We explain the successes and difficulties encountered during the design and implementation of Probapop

EmergencyGrid:Planning in Convergence Environments

Government agencies are often responsible for event handling, planning, coordination, and status reporting during emergency response in natural disaster events such as floods, tsunamis and earthquakes. Across such a range of emergency response scenarios, there is a common set of requirements that distributed intelligent computer systems generally address. To support the implementation of these requirements, some researchers are proposing the creation of grids, where final interface and processing nodes perform joint work supported by a network infrastructure. The aim of this project is to extend the concepts of emergency response grids, using a convergence scenario between web and other computational platforms. Our initial work focuses on the Interactive Digital TV platform, where we intend to transform individual TV devices into active final nodes, using a hierarchical planning structure. We describe the architecture of this approach and an initial prototype specification that is being developed to validate some concepts and illustrate the advantages of this convergence planning environment

Exploring Knowledge Engineering Strategies in Designing and Modelling a Road Traffic Accident Management Domain

Formulating knowledge for use in AI Planning engines is currently something of an ad-hoc process, where the skills of knowledge engineers and the tools they use may significantly influence the quality of the resulting planning application. There is little in the way of guidelines or standard procedures, however, for knowledge engineers to use when formulating knowledge into planning domain languages such as PDDL. This paper seeks to investigate this process using as a case study a road traffic accident management domain. Managing road accidents requires systematic, sound planning and coordination of resources to improve outcomes for accident victims. We have derived a set of requirements in consultation with stakeholders for the resource coordination part of managing accidents. We evaluate two separate knowledge engineering strategies for encoding the resulting planning domain from the set of requirements: (a) the traditional method of PDDL experts and text editor, and (b) a leading planning GUI with built in UML modelling tools. These strategies are evaluated using process and product metrics, where the domain model (the product) was tested extensively with a range of planning engines. The results give insights into the strengths and weaknesses of the approaches, highlight lessons learned regarding knowledge encoding, and point to important lines of research for knowledge engineering for planning

Progress in AI Planning Research and Applications

Planning has made significant progress since its inception in the 1970s, in terms both of the efficiency and sophistication of its algorithms and representations and its potential for application to real problems. In this paper we sketch the foundations of planning as a sub-field of Artificial Intelligence and the history of its development over the past three decades. Then some of the recent achievements within the field are discussed and provided some experimental data demonstrating the progress that has been made in the application of general planners to realistic and complex problems. The paper concludes by identifying some of the open issues that remain as important challenges for future research in planning

Portfolio-based Planning: State of the Art, Common Practice and Open Challenges

In recent years the field of automated planning has significantly advanced and several powerful domain-independent planners have been developed. However, none of these systems clearly outperforms all the others in every known benchmark domain. This observation motivated the idea of configuring and exploiting a portfolio of planners to perform better than any individual planner: some recent planning systems based on this idea achieved significantly good results in experimental analysis and International Planning Competitions. Such results let us suppose that future challenges of the Automated Planning community will converge on designing different approaches for combining existing planning algorithms. This paper reviews existing techniques and provides an exhaustive guide to portfolio-based planning. In addition, the paper outlines open issues of existing approaches and highlights possible future evolution of these techniques

Towards a Protocol for Benchmark Selection in IPC

The planning competition has traditionally played an important role in motivating research and advances in Planning & Scheduling techniques. Despite its pivotal role in the planning community, some aspects of the competition have not been engineered yet. This is the case for the protocol for selecting benchmark instances. Benchmarks are of critical importance, since they can significantly affect competition results. In this paper we describe desirable properties of a selection protocol, discuss methods exploited in past SAT and planning competitions, and identify challenges that organisers of future competitions have to address in order to improve reliability and usefulness of the insights gained by looking at competitions’ results

Integration of Forecasting, Scheduling, Machine Learning, and Efficiency Improvement Methods into the Sport Management Industry

Sport management is a complicated and economically impactful industry and involves many crucial decisions: such as which players to retain or release, how many concession vendors to add, how many fans to expect, what teams to schedule, and many others are made each offseason and changed frequently. The task of making such decisions effectively is difficult, but the process can be made easier using methods of industrial and systems engineering (ISE). Integrating methods such as forecasting, scheduling, machine learning, and efficiency improvement from ISE can be revolutionary in helping sports organizations and franchises be consistently successful. Research shows areas including player evaluation, analytics, fan attendance, stadium design, accurate scheduling, play prediction, player development, prevention of cheating, and others can be improved when ISE methods are used to target inefficient or wasteful areas

On the Effective Configuration of Planning Domain Models

The development of domain-independent planners within the AI Planning community is leading to “off the shelf” technology that can be used in a wide range of applications. Moreover, it allows a modular approach – in which planners and domain knowledge are modules of larger software applications – that facilitates substitutions or improvements of individual modules without changing the rest of the system. This approach also supports the use of reformulation and configuration techniques, which transform how a model is represented in order to improve the efficiency of plan generation. In this paper, we investigate how the performance of planners is affected by domain model configuration. We introduce a fully automated method for this configuration task, and show in an extensive experimental analysis with six planners and seven domains that this process (which can, in principle, be combined with other forms of reformulation and configuration) can have a remarkable impact on performance across planners. Furthermore, studying the obtained domain model configurations can provide useful information to effectively engineer planning domain models