Learning STRIPS Action Models with Classical Planning

This paper presents a novel approach for learning STRIPS action models from examples that compiles this inductive learning task into a classical planning task. Interestingly, the compilation approach is flexible to different amounts of available input knowledge; the learning examples can range from a set of plans (with their corresponding initial and final states) to just a pair of initial and final states (no intermediate action or state is given). Moreover, the compilation accepts partially specified action models and it can be used to validate whether the observation of a plan execution follows a given STRIPS action model, even if this model is not fully specified.Comment: 8+1 pages, 4 figures, 6 table

Assembling learning objects for personalized learning. An AI planning perspective

© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] The aim of educational systems is to assemble learning objects on a set of topics tailored to the goals and individual students' styles. Given the amount of available Learning Objects, the challenge of e-learning is to select the proper objects, define their relationships, and adapt their sequencing to the specific needs, objectives, and background of the student. This article describes the general requirements for course adaptation, the full potential of applying planning techniques on the construction of personalized e-learning routes, and how to accommodate the temporal and resource constraints to make the course applicable in a real scenario.This work has been partially supported by the Spanish Ministerio de Ciencia e Innovacion (MICINN) under projects TIN2008-06701-C03-01, Consolider Ingenio 2010 CSD2007-00022, and the Valencian Prometeo project 2008/051.Garrido, A.; Onaindia De La Rivaherrera, E. (2013). Assembling learning objects for personalized learning. An AI planning perspective. IEEE Intelligent Systems. 28(2):64-73. https://doi.org/10.1109/MIS.2011.36S647328

Context-Aware Multi-Agent Planning in intelligent environments

A system is context-aware if it can extract, interpret and use context information and adapt its functionality to the current context of use. Multi-agent planning generalizes the problem of planning in domains where several agents plan and act together, and share resources, activities, and goals. This contribution presents a practical extension of a formal theoretical model for Context-Aware Multi-Agent Planning based upon an argumentationbased defeasible logic. Our framework, named CAMAP, is implemented on a platform for open multiagent systems and has been experimentally tested, among others, in applications of ambient intelligence in the field of health-care. CAMAP is based on a multi-agent partial-order planning paradigm in which agents have diverse abilities, use an argumentation-based defeasible contextual reasoning to support their own beliefs and refute the beliefs of the others according to their context knowledge during the plan search process. CAMAP shows to be an adequate approach to tackle ambient intelligence problems as it gathers together in a single framework the ability of planning while it allows agents to put forward arguments that support or argue upon the accuracy, unambiguity and reliability of the context-aware information.This work is mainly supported by the Spanish Ministry of Science and Education under the FPU Grant Reference AP2009-1896 awarded to Sergio Pajares Ferrando, and Projects, TIN2011-27652-C03-01, and Consolider Ingenio 2010 CSD2007-00022.Pajares Ferrando, S.; Onaindia De La Rivaherrera, E. (2013). Context-Aware Multi-Agent Planning in intelligent environments. Information Sciences. 227:22-42. https://doi.org/10.1016/j.ins.2012.11.021S224222

Multimodal Classification of Teaching Activities from University Lecture Recordings

[EN] The way of understanding online higher education has greatly changed due to the worldwide pandemic situation. Teaching is undertaken remotely, and the faculty incorporate lecture audio recordings as part of the teaching material. This new online teaching-learning setting has largely impacted university classes. While online teaching technology that enriches virtual classrooms has been abundant over the past two years, the same has not occurred in supporting students during online learning. To overcome this limitation, our aim is to work toward enabling students to easily access the piece of the lesson recording in which the teacher explains a theoretical concept, solves an exercise, or comments on organizational issues of the course. To that end, we present a multimodal classification algorithm that identifies the type of activity that is being carried out at any time of the lesson by using a transformer-based language model that exploits features from the audio file and from the automated lecture transcription. The experimental results will show that some academic activities are more easily identifiable with the audio signal while resorting to the text transcription is needed to identify others. All in all, our contribution aims to recognize the academic activities of a teacher during a lesson.This research was funded by the project CAR: Classroom Activity Recognition of GENERALITAT VALENCIANA. CONSELLERIA D'EDUCACIO grant number PROMETEO/2019/111.Sapena Vercher, O.; Onaindia De La Rivaherrera, E. (2022). Multimodal Classification of Teaching Activities from University Lecture Recordings. Applied Sciences. 12(9):1-18. https://doi.org/10.3390/app1209478511812

Defeasible-argumentation-based multi-agent planning

[EN] This paper presents a planning system that uses defeasible argumentation to reason about context information during the construction of a plan. The system is designed to operate in cooperative multi-agent environments where agents are endowed with planning and argumentation capabilities. Planning allows agents to contribute with actions to the construction of the plan, and argumentation is the mechanism that agents use to defend or attack the planning choices according to their beliefs. We present the formalization of the model and we provide a novel specification of the qualification problem. The multi-agent planning system, which is designed to be domain-independent, is evaluated with two planning tasks from the problem suites of the International Planning Competition. We compare our system with a non-argumentative planning framework and with a different approach of planning and argumentation. The results will show that our system obtains less costly and more robust solution plans.This work has been partly supported by the Spanish MINECO under project TIN2014-55637-C2-2-R and the Valencian project PROMETEO II/2013/019.Pajares Ferrando, S.; Onaindia De La Rivaherrera, E. (2017). Defeasible-argumentation-based multi-agent planning. Information Sciences. 411:1-22. https://doi.org/10.1016/j.ins.2017.05.014S12241

A Decentralized Multi-Agent Coordination Method for Dynamic and Constrained Production Planning

[EN] In the capacitated production planning problem, quantities of products need to be determined at consecutive periods within a given time horizon when product demands, costs, and production capacities vary through time. We focus on a general formulation of this problem where each product is produced in one step and setup cost is paid at each period of production. Additionally, products can be anticipated or backordered in respect to the demand period. We propose a computationally efficient decentralized approach based on the spillover effect relating to the accumulation of production costs of each product demand through time. The performance of the spillover algorithm is compared against the state-of-the-art mixed integer programming branch-and-bound solver CPLEX 12.8 considering optimality gap and computational time.This work is supported by: the Spanish MINECO projects RTI2018-095390-B-C33 (MCIU/AEI/FEDER, UE) and TIN2017- 88476-C2-1-R, the French ADEME project E-Logistics, and an STSM Grant funded by the European ICT COST Action IC1406, cHiPSet.Lujak, M.; Fernandez, A.; Onaindia De La Rivaherrera, E. (2020). A Decentralized Multi-Agent Coordination Method for Dynamic and Constrained Production Planning. International Foundation for Autonomous Agents and Multiagent Systems. 1913-1915. http://hdl.handle.net/10251/179784S1913191