Exploring the Synergy between two Modular Learning Techniques for Automated Planning

In the last decade the emphasis on improving the operational performance of domain independent automated planners has been in developing complex techniques which merge a range of different strategies. This quest for operational advantage, driven by the regular international planning competitions, has not made it easy to study, understand and predict what combinations of techniques will have what effect on a planner’s behaviour in a particular application domain. In this paper, we consider two machine learning techniques for planner performance improvement, and exploit a modular approach to their combination in order to facilitate the analysis of the impact of each individual component. We believe this can contribute to the development of more transparent planning engines, which are designed using modular, interchangeable, and well-founded components. Specifically, we combined two previously unrelated learning techniques, entanglements and relational decision trees, to guide a “vanilla” search algorithm. We report on a large experimental analysis which demonstrates the effectiveness of the approach in terms of performance improvements, resulting in a very competitive planning configuration despite the use of a more modular and transparent architecture. This gives insights on the strengths and weaknesses of the considered approaches, that will help their future exploitation

Educación Intercultural en un aula de Educación Infantil

En este trabajo se pretende resaltar el valor que posee la Educación Intercultural para el crecimiento personal de los individuos, así como explicar el porqué es importante educar en los principios del interculturalismo desde los primeros años en la escuela. Se expone un ejemplo de cómo llevar la Educación Intercultural a un aula de Educación Infantil, con el diseño de unidad didáctica centrada en el tema de la cultura en la que se utilizan recursos y estrategias específicas de esa etapa.Grado en Educación Infanti

¿Te atreves a experimentar? Propuesta didáctica para implementar el enfoque CTS y la ciencia en 3º de Educación Primaria

La ciencia, la tecnología y la sociedad siempre han estado unidas, para poder comprender esta vinculación necesitamos tener una cultura científica. Ante la presencia actual de la tecnología en nuestra sociedad, se hace necesaria no solo una cultura científica sino una cultura científica-tecnológica. La labor de la educación es enseñar esta asociación para presentar como los avances tecnológicos y científicos nos han hecho crecer como sociedad y a su vez como la sociedad ha provocado estos progresos. La Educación Científica es la encargada de formar científicamente al alumnado y de tratar la conexión existente entre la ciencia, la tecnología y la sociedad. En este documento se presenta una secuencia didáctica para un grupo de 14 alumnos de 3º de Educación Primaria cuyo objetivo es hacer consciente al alumnado de la presencia de la ciencia en su día a día (cocina, publicidad). Dadas la edad y las características específicas del grupo, se ha considerado el juego como enfoque didáctico más adecuado para las actividades experimentales.Grado en Educación Primari

Automatic Compilation of Objects to Counters in Automatic Planning. Case of study: Creation Planning

In classical planning, all objects should be represented as constants explicitly, even though their names could be irrelevant, which produces severe instantiation problems. This is specially problematic in tasks with actions for creating new objects, as it involves to estimate how many potential new objects will be necessary to solve the task. We propose a new automatic compilation from the classical to a numeric planning model to represent objects with irrelevant names using numerical functions. The compilation reduces the size of the instantiation and avoids the need of estimating the number of future objects in advance. The compiled planning task can be solved several orders of magnitude faster than its equivalent classical model.No publicad

PLTOOL: a knowledge engineering tool for planning and learning

Artificial intelligence (AI) planning solves the problem of generating a correct and efficient ordered set of instantiated activities, from a knowledge base of generic actions, which when executed will transform some initial state into some desirable end-state. There is a long tradition of work in AI for developing planners that make use of heuristics that are shown to improve their performance in many real world and artificial domains. The developers of planners have chosen between two extremes when defining those heuristics. The domain-independent planners use domain-independent heuristics, which exploit information only from the ‘syntactic’ structure of the problem space and of the search tree. Therefore, they do not need any ‘semantic’ information from a given domain in order to guide the search. From a knowledge engineering (KE) perspective, the planners that use this type of heuristics have the advantage that the users of this technology need only focus on defining the domain theory and not on defining how to make the planner efficient (how to obtain ‘good’ solutions with the minimal computational resources). However, the domain-dependent planners require users to manually represent knowledge not only about the domain theory, but also about how to make the planner efficient. This approach has the advantage of using either better domain-theory formulations or using domain knowledge for defining the heuristics, thus potentially making them more efficient. However, the efficiency of these domain-dependent planners strongly relies on the KE and planning expertise of the user. When the user is an expert on these two types of knowledge, domain-dependent planners clearly outperform domain-independent planners in terms of number of solved problems and quality of solutions. Machine-learning (ML) techniques applied to solve the planning problems have focused on providing middle-ground solutions as compared to the aforementioned two extremes. Here, the user first defines a domain theory, and then executes the ML techniques that automatically modify or generate new knowledge with respect to both the domain theory and the heuristics. In this paper, we present our work on building a tool, PLTOOL (planning and learning tool), to help users interact with a set of ML techniques and planners. The goal is to provide a KE framework for mixed-initiative generation of efficient and good planning knowledge.This work has been partially supported by the Spanish MCyT project TIC2002-04146-C05-05, MEC project TIN2005-08945-C06-05 and regional CAM-UC3M project UC3M-INF-05-016.Publicad

On the application of classical planning to real social robotic tasks

Pittsburgh, USA (19-20 June 2017)Automated Planning is now a mature area offering several techniques and search heuristics extremely useful to solve problems in realistic domains. However, its application to real and dynamic environments as Social Robotics requires much work focused, not only in the efficiency of the planners, but also in tractable task modeling and efficient execution and monitoring of the plan into the robotic control architecture. This paper identifies the main issues that must be taken into account while using classical Automated Planning for the control of a social robot and contributes some practical solutions to overcome such inherent difficulties. Some of them are the discrimination between predicates for internal control and external sensing, the concept of predicted nominal behavior with corrective actions or plans, the continuous monitoring of the plan execution and the handling of action interruptions. This manuscript highlights the dependencies between all the design and deployment activities involved: task modeling, plan generation, and action execution and monitoring. A task of Comprehensive Geriatric Assessment (CGA) is used as an illustrative example that can be easily generalized to any other interactive task

Un Simulador empresarial como herramienta práctica para la asignatura de Aprendizaje Automático

En este artículo se plantea el uso de un simulador empresarial (SIMBA) como dominio de aplicación de las prácticas de la asignatura de Aprendizaje Automático (AA). El objetivo principal es que los alumnos se enfrenten a un entorno realista en el que tengan que plantear y ofrecer soluciones basadas en AA. El contexto de la toma de decisiones empresarial ofrece un amplio abanico de aplicaciones, desde el análisis de datos (predicción de resultados empresariales, análisis de perfiles de decisión, etc.) hasta el aprendizaje de comportamientos (soporte a la toma de decisión). Esto permite configurar la parte práctica de la asignatura mediante prácticas solapadas con un matiz mucho más realista que el que se conseguía en años anteriores, a la vez que se cubren gran parte de los contenidos teóricos de la asignatura. La iniciativa se aplicó el curso 2008-2009 con resultados satisfactorios y forma parte de un proyecto de innovación docente que se está realizando en el curso 2009-2010.Peer Reviewe

Challenges on the application of automated planning for comprehensive geriatric assessment using an autonomous social robot

November 22-23, 2018, Madrid, SpainComprehensive Geriatric Assessment is a medical procedure to evaluate the physical, social and psychological status of elder patients. One of its phases consists of performing different tests to the patient or relatives. In this paper we present the challenges to apply Automated Planning to control an autonomous robot helping the clinician to perform such tests. On the one hand the paper focuses on the modelling decisions taken, from an initial approach where each test was encoded using slightly different domains, to the final unified domain allowing any test to be represented. On the other hand, the paper deals with practical issues arisen when executing the plans. Preliminary tests performed with real users show that the proposed approach is able to seamlessly handle the patient-robot interaction in real time, recovering from unexpected events and adapting to the users' preferred input method, while being able to gather all the information needed by the clinician.This work has been partially funded by the European Union ECHORD++ project (FP7-ICT-601116) and the TIN2015-65686-C5 Spanish Ministerio de Economía y Competitividad project. Javier García is partially supported by the Comunidad de Madrid (Spain) funds under the project 2016-T2/TIC-1712