Creating planning portfolios with predictive models

Mención Internacional en el título de doctorSequential planning portfolios are very powerful in exploiting the complementary strength of different automated planners: for each planning task there are one or more base planners that obtain the best solution. Therefore, the main challenge when building a planning portfolio is to ensure that a suitable planner be chosen and that it gets enough planning time. To solve this problem we need firstly to define three elements. The first is the settings or planning conditions: time, memory, or other constraints. The second one is the set of base planners. And finally, a benchmark that provides us with knowledge on how the base planners will behave under the given settings, following some kind of inductive process. Ideally, if the previous elements are correctly defined, when a new planning task arrives, an oracle will be able to tell which base planner to run and for how long. In practice, since no oracle exists, the challenge to choose a sub-set of base planners, is assigning them a running time and deciding the order in which they are run to optimize a planning metric under the predefined settings. Many state-of-the- art portfolios might never achieve an optimal performance because they do not select different planners for the different planning tasks. In addition, these static techniques typically assign a fixed running time to the selected set of planners, independently of the task. besides, the old-fashioned dynamic portfolios present a poor characterization of the planning task and do not have enough knowledge to predict an accurate portfolio configuration in many cases. The aforementioned drawbacks are intensified by the fact that there is an increasing number of planners available to choose from, although many of them are designed following similar approaches, so they are expected to behave similarly. This dissertation is built on two main hypotheses. Firstly that the space of the base planners can be reduced just by selecting a subset of diverse or complementary planners; e.g. that there is a minimal set of planners that ensure that the optimal portfolio can be computed. Secondly, that planning tasks can be characterized, and that the difficulty in solving them can be modelled as a function of these features. To evaluate the first hypothesis, we analyze different metrics that could be used to filter the initial set of base planners. Classical metrics such as coverage, quality or execution time have been chosen by different portfolios in the past. We demonstrate that these selection methods may reduce the diversity of the portfolios, and propose an alternative method based on the Pareto dominance. We then carry out a profound analysis on previous planning task characterizations and show how we could exploit them in current planning paradigms. A group of very informative features are proposed to improve the current feature definition of the planning tasks. These features have enough knowledge to differentiate planning tasks with similar \a priori" complexity. In this thesis we demonstrate that the implicit knowledge can be exploited in the construction of predictive models. These models estimate whether a base planner will be able to solve a given problem and, if so, how long it will take. Nevertheless, the predictive models are not perfect and sometimes provide wrong (or inaccurate) predictions. To solve this kind of problems, we propose different portfolio strategies to combine the number of selected base planners and their times. These strategies take into account the predefined settings and the knowledge learned in previous phases. In conclusion, this thesis sets out a profound analysis of three different mechanisms or steps to create planning portfolios with predictive models, including new proposals for developing: planner filtering, planning task featuring, learning predictive models and portfolio construction strategies. One of the proposed portfolios was the winner of the Sequential Satisficing Track of the International Planning Competition held in 2014Los portfolios de planificadores tienen un gran potencial ya que pueden aprovecharse de los diferentes planificadores automáticos, consiguiendo mejorar el rendimiento de un único planificador. Sin embargo, la creación de un portfolio no es una tarea sencilla, ya que para poder crear uno lo suficientemente bueno, hay que tratar tres problemas fundamentales. El primero de ellos es encontrar qué planificadores hay que seleccionar como componentes del mismo. La segunda es el tiempo que hay que asignar a cada planificador y, la última y no menos importante el orden en el que se tienen que ejecutar. Actualmente en el estado del arte, estas configuraciones, se realizan a partir de los resultados obtenidos por los planificadores en una fase previa de entrenamiento con un conjunto de problemas y restricciones prefijado (tiempo, memoria, etc), consiguiendo una configuración específica tratando de optimizar una métrica. Idealmente, la mejor configuración posible consiste en asignar el tiempo suficiente al mejor planificador para cada tarea de planificación. Sin embargo, esta configuración no siempre es posible, y hay que recurrir a otras aproximaciones como asignar un tiempo fijo a una selección de planificadores. Ésta no es la única simplificación utilizada, existen otras técnicas más cercanas a la óptima, en las cuales se selecciona un planificador o varios en función de la tarea a resolver. Sin embargo, estos sistemas, denominados dinámicos, incluyen una escasa caracterización de las tareas de planificación. En esta tesis se parte de dos hipótesis. La primera de ellas es que existe un conjunto reducido de planificadores que maximiza la diversidad. La segunda de ellas consiste en la posibilidad de crear un conjunto de descriptivos lo suficientemente bueno para caracterizar la tarea de planificación. La caracterización de las tareas de planificación puede estar basada en sus distintas representaciones, así como en sus paradigmas. La primera tarea es seleccionar un conjunto de planificadores; realizando un análisis basado en las métricas clásicas de planificación, como son problemas resueltos, calidad y tiempo para seleccionar un subconjunto de planificadores. Adicionalmente, proponemos como alternativa a estas métricas, una técnica multiobjetivo. Este criterio está basado en la dominancia de Pareto combinando las métricas de tiempo y calidad. Continuando con nuestras hip_otesis es necesario crear un conjunto de características bien informado para la tarea de planificación. Estas características deben ser capaces de diferenciar adecuadamente por problema y para ello sería necesario basarse en los distintos paradigmas de la planificación automática. Este grupo de características tienen que ser úutiles para crear modelos predictivos. Estos modelos podrán darnos además de una selección de planificadores, una aproximación del tiempo asignado a cada componente y el orden de los mismos. Adicionalmente se presentarán una serie de estrategias para explotar el conocimiento obtenido con los modelos predictivos. En conclusión, se plantea y desarrolla un sistema para configurar porfolios de planificadores usando modelos predictivos en tres fases distintas. Una instanciación de este sistema fue el ganador de la competición internacional de planificación en el áarea de satisfacibilidad en el año 2014.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: María Araceli Sanchís de Miguel.- Secretario: Álvaro Torralba Arias de Reyna.- Vocal: Alessandro Saett

Planning & Scheduling Applications in Urban Traffic Management

Local authorities that manage traffic-related issues in urban areas have to optimise the use of available resources, in order to minimise congestion and delays. In this context, Automated Planning and Scheduling can be fruitfully exploited, in order to provide dynamic plans that help managing the urban road network. In this paper we provide a review of existing planning and scheduling approaches that have been designed for dealing with different aspects of traffic management, with the aim of gaining insights on the limits of current applications, and highlighting the open challenges

Desarrollo y selección de características basadas en distancias entre grafos para problemas de predicción en planificación automática

El objetivo del presente proyecto es el desarrollo y la selección de características basadas en la distancia entre grafos para problemas de predicción en planificación automática. En la planificación automática existen dos componentes: los dominios y los problemas. De estos elementos se puede generar diversa información, como el número de objetos, número de predicados, la longitud de los planes que los resuelven o el número de nodos generados en el proceso de cómputo del plan. Además de esta información se va a utilizadar una serie de grafos que permiten comparar distintos problemas. Estos grafos se basan en la representación SAS+, que consiste en una lógica proposicional que posee variables de estado multivaluadas con el fin de expresar una estructura causal subyacente. Con todos los datos que se han obtenido, se ha creado una representación común independiente del dominio de planificación. Después se van a realizar una serie de comparaciones entre el problema a resolver y la base de hechos (problemas con solución), creando para ello unas medidas de distancia para los grafos. Estas comparaciones han sido almacenadas para su posterior recuperación. Todo el conocimiento generado se puede utilizar para generar modelos de predicción o para el razonamiento basado en casos. Y con estos modelos se ha comprobado si las medidas desarrolladas con los grafos son acertadas. Además se han realizado procesos de selección de características de los datos de los problemas, para concluir qué variables son las más relevantes para los problemas de planificación automática. Las principales conclusiones obtenidas de este proyecto son la aportación de las características más significativas, además de comprobar que la medida de distancia de los grafos es acertada para la creación de los modelos. ______________________________________________________________________________________________________________________________________The objective of this project is to explain the development and selection of features based on the distance between graphs for prediction problems in automated planning. In automatic planning, there are two fundamental components, the domains and problems. Of these elements can generate various information such as the number of objects, number of predicates, the length of the plans that meet or the number of nodes generated in the process of computing the plan. Addition to this information, we used a series of graphs. These graphs are based on the representation SAS+, which is a propositional logic that has multi-valued state variables in order to express an underlying causal structure. With all the gathered data, a domain-independent representation has been developed. Then they will make a series of comparisons between two di fferent problems, thus creating some distance measures for graphs. These comparisons have been stored for later retrieval. All the knowledge generated can be used to generate predictive models or case-based reasoning. And with these models, wether the measures developed in the graphs are correct has been checked. In addition, feature selection processes have been developed, to conclude which variables are relevant to the problems of automatic planning. The most important conclusions from this project are providing the most signi cant features and to verify that the graph based distance measure are accurate for creating prediction models.Ingeniería en Informátic

The IBaCoP planning system: instance-based configured portfolios

Sequential planning portfolios are very powerful in exploiting the complementary strength of different automated planners. The main challenge of a portfolio planner is to define which base planners to run, to assign the running time for each planner and to decide in what order they should be carried out to optimize a planning metric. Portfolio configurations are usually derived empirically from training benchmarks and remain fixed for an evaluation phase. In this work, we create a per-instance configurable portfolio, which is able to adapt itself to every planning task. The proposed system pre-selects a group of candidate planners using a Pareto-dominance filtering approach and then it decides which planners to include and the time assigned according to predictive models. These models estimate whether a base planner will be able to solve the given problem and, if so, how long it will take. We define different portfolio strategies to combine the knowledge generated by the models. The experimental evaluation shows that the resulting portfolios provide an improvement when compared with non-informed strategies. One of the proposed portfolios was the winner of the Sequential Satisficing Track of the International Planning Competition held in 2014.We thank the authors of the base planners because our work is based largely on their previous effort. This work has been partially supported by the Spanish projects TIN2011-27652-C03-02, TIN2012-38079-C03-02 and TIN2014-55637-C2-1-R

Performance modelling of planners from homogeneous problem sets

Empirical performance models play an important role in the development of planning portfolios that make a per-domain or per-problem configuration of its search components. Even though such portfolios have shown their power when compared to other systems in current benchmarks, there is no clear evidence that they are capable to differentiate problems (instances) having similar input properties (in terms of objects, goals, etc.) but fairly different runtime for a given planner. In this paper we present a study of empirical performance models that are trained using problems having the same configuration, with the objective of guiding the models to recognize the underlying differences existing among homogeneous problems. In addition we propose a set of new features that boost the prediction capabilities under such scenarios. The results show that the learned models clearly performed over random classifiers, which reinforces the hypothesis that the selection of planners can be done on a per-instance basis when configuring a portfolio.This work has been partially supported by the Spanish projects TIN2014-55637-C2-1-R and TIN2015-65686-C5-1-R

Performance Modelling of Planners from Homogeneous Problem Sets

Empirical performance models play an important role in the development of planning portfolios that make a per-domain or per-problem configuration of its search components. Even though such portfolios have shown their power when compared to other systems in current benchmarks, there is no clear evidence that they are capable to differentiate problems (instances) having similar input properties (in terms of objects, goals, etc.) but fairly different runtime for a given planner. In this paper we present a study of empirical performance models that are trained using problems having the same configuration, with the objective of guiding the models to recognize the underlying differences existing among homogeneous problems. In addition we propose a set of new features that boost the prediction capabilities under such scenarios. The results show that the learned models clearly performed over random classifiers, which reinforces the hypothesis that the selection of planners can be done on a per-instance basis when configuring a portfolio

Educación Infantil : desarrollo curricular del ciclo formativo de grado superior de FP

NIPO: 176-96-113-1Este material curricular contiene la definición y el desarrollo de los procesos de enseñanza-aprendizaje de Educación infantil, ciclo formativo de grado superior de la Formación Profesional Específica (FPE). Para su confección se ha partido de los elementos recogidos en los correspondientes Reales Decretos de enseñanzas mínimas y del currículo del MEC. Tiene la finalidad de orientar al profesorado que imparte las enseñanzas de FPE contempladas en la LOGSE. Estos materiales son programaciones precisas que pueden ser adaptadas y aplicadas por los profesores de forma directa. Los elementos curriculares se presentan ordenados en un conjunto de fichas, cada una de las cuales se corresponde con una unidad de trabajo. A pesar de ser un producto casi acabado, los materiales tienen un carácter experimental, pudiendo ser depurados y perfeccionados mediante el contraste con la práctica docente. Los módulos tratados son: 1.- Didáctica de la educación infantil. 2.- Autonomía personal y salud. 3.- Metodología del juego. 4.- Expresión y comunicación. 5.- Desarrollo cognitivo y motor. 6.- Desarrollo socio-afectivo e intervención con familias. 7.- Animación y dinámica de grupos.MadridBiblioteca de Educación del Ministerio de Educación, Cultura y Deporte; Calle San Agustín 5 -3 Planta; 28014 Madrid; Tel. +34917748000; [email protected]

Cooperative Multi-Agent Planning: A survey

[EN] Cooperative multi-agent planning (MAP) is a relatively recent research field that combines technologies, algorithms, and techniques developed by the Artificial Intelligence Planning and Multi-Agent Systems communities. While planning has been generally treated as a single-agent task, MAP generalizes this concept by considering multiple intelligent agents that work cooperatively to develop a course of action that satisfies the goals of the group. This article reviews the most relevant approaches to MAP, putting the focus on the solvers that took part in the 2015 Competition of Distributed and Multi-Agent Planning, and classifies them according to their key features and relative performance.This work is supported by the GLASS project Grant No. TIN2014-55637-C2-2-R MINECO of the Spanish Ministerio de Economia, Industria y Competitividad, the Prometeo project II/2013/019 funded by the Valencian Government, and the four-year FPI-UPV research scholarship granted to the first author by the Universitat Politecnica de Valencia. Additionally, this research was partially supported by the Czech Science Foundation under Grant No. 15-20433Y CSF.Torreño Lerma, A.; Onaindia De La Rivaherrera, E.; Komenda, A.; Tolba, M. (2017). Cooperative Multi-Agent Planning: A survey. ACM Computing Surveys. 50(6):84:1-84:32. https://doi.org/10.1145/3128584S84:184:32506Eyal Amir and Barbara Engelhardt. 2003. Factored planning. In Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI’03), Vol. 3. 929--935.J. Benton, Amanda J. Coles, and Andrew I. Coles. 2012. Temporal planning with preferences and time-dependent continuous costs. In Proceedings of the 22nd International Conference on Automated Planning and Scheduling (ICAPS’12). 2--10.Andrea Bonisoli, Alfonso E. Gerevini, Alessandro Saetti, and Ivan Serina. 2014. A privacy-preserving model for the multi-agent propositional planning problem. In Proceedings of the 21st European Conference on Artificial Intelligence (ECAI’14). 973--974.Daniel Borrajo. 2013. Multi-agent planning by plan reuse. In Proceedings of the 12th International Conference on Autonomous Agents and Multi-agent Systems (AAMAS’13). 1141--1142.Daniel Borrajo and Susana Fernández. 2015. MAPR and CMAP. In Proceedings of the Competition of Distributed and Multi-Agent Planners (CoDMAP’15). 1--3.Craig Boutilier and Ronen I. Brafman. 2001. Partial-order planning with concurrent interacting actions. J. Artific. Intell. Res. 14 (2001), 105--136.Ronen I. Brafman. 2015. A privacy preserving algorithm for multi-agent planning and search. In Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI’15). 1530--1536.Ronen I. Brafman and Carmel Domshlak. 2006. Factored planning: How, when, and when not. In Proceedings of the 21st National Conference on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conference. 809--814.Ronen I. Brafman and Carmel Domshlak. 2008. From one to many: Planning for loosely coupled multi-agent systems. In Proceedings of the 18th International Conference on Automated Planning and Scheduling (ICAPS’08). 28--35.Isabel Cenamor, Tomás de la Rosa, and Fernando Fernández. 2014. IBACOP and IBACOP2 planner. In Proceedings of the International Planning Competition (IPC’14).Bradley J. Clement and Edmund H. Durfee. 1999. Top-down search for coordinating the hierarchical plans of multiple agents. In Proceedings of the 3rd Annual Conference on Autonomous Agents (AGENTS’99). ACM, New York, NY, 252--259.Daniel D. Corkill. 1979. Hierarchical planning in a distributed environment. In Proceedings of the 6th International Joint Conference on Artificial Intelligence (IJCAI’79). 168--175.Jeffrey S. Cox and Edmund H. Durfee. 2004. Efficient mechanisms for multiagent plan merging. In Proceedings of the 3rd Conference on Autonomous Agents and Multiagent Systems (AAMAS’04). 1342--1343.Jeffrey S. Cox and Edmund H. Durfee. 2009. Efficient and distributable methods for solving the multiagent plan coordination problem. Multiagent Grid Syst. 5, 4 (2009), 373--408.Matthew Crosby, Anders Jonsson, and Michael Rovatsos. 2014. A single-agent approach to multiagent planning. In Proceedings of the 21st European Conference on Artificial Intelligence (ECAI’14). 237--242.Matthew Crosby, Michael Rovatsos, and Ronald P. A. Petrick. 2013. Automated agent decomposition for classical planning. In Proceedings of the 23rd International Conference on Automated Planning and Scheduling (ICAPS’13). 46--54.Mathijs de Weerdt, André Bos, Hans Tonino, and Cees Witteveen. 2003. A resource logic for multi-agent plan merging. Ann. Math. Artific. Intell. 37, 1-2 (2003), 93--130.Mathijs de Weerdt and Bradley J. Clement. 2009. Introduction to planning in multiagent systems. Multiagent Grid Syst. 5, 4 (2009), 345--355.Keith Decker, Salim Khan, Carl Schmidt, Gang Situ, Ravi Makkena, and Dennis Michaud. 2002. BioMAS: A multi-agent system for genomic annotation. Int. J. Coop. Info. Syst. 11, 3 (2002), 265--292.Keith Decker and Victor R. Lesser. 1992. Generalizing the partial global planning algorithm. Int. J. Coop. Info. Syst. 2, 2 (1992), 319--346.Marie desJardins and Michael Wolverton. 1999. Coordinating a distributed planning system. AI Mag. 20, 4 (1999), 45--53.Marie E. desJardins, Edmund H. Durfee, Charles L. Ortiz, and Michael J. Wolverton. 1999. A survey of research in distributed continual planning. AI Mag. 20, 4 (1999), 13--22.Yannis Dimopoulos, Muhammad A. Hashmi, and Pavlos Moraitis. 2012. -SATPLAN: Multi-agent planning as satisfiability. Knowl.-Based Syst. 29 (2012), 54--62.Jürgen Dix, Héctor Muñoz-Avila, Dana S. Nau, and Lingling Zhang. 2003. IMPACTing SHOP: Putting an AI planner into a multi-agent environment. Ann. Math. Artific. Intell. 37, 4 (2003), 381--407.Edmund H. Durfee. 1999. Distributed Problem Solving and Planning, Gerhard Weiss (ed.). MIT Press,118--149.Edmund H. Durfee and Victor Lesser. 1991. Partial global planning: A coordination framework for distributed hypothesis formation. IEEE Trans. Syst. Man Cybernet. Spec. Issue Distrib. Sensor Netw. 21, 5 (1991), 1167--1183.Eithan Ephrati and Jeffrey S. Rosenschein. 1994. Divide and conquer in multi-agent planning. In Proceedings of the 12th National Conference on Artificial Intelligence (AAAI’94). 375--380.Eithan Ephrati and Jeffrey S. Rosenschein. 1997. A heuristic technique for multi-agent planning. Ann. Math. Artific. Intell. 20, 1–4 (1997), 13--67.Eric Fabre, Loïg Jezequel, Patrik Haslum, and Sylvie Thiébaux. 2010. Cost-optimal factored planning: Promises and pitfalls. In Proceedings of the 20th International Conference on Automated Planning and Scheduling (ICAPS’10). 65--72.Boi Faltings, Thomas Léauté, and Adrian Petcu. 2008. Privacy guarantees through distributed constraint satisfaction. In Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT’08), Vol. 2. IEEE, 350--358.Richard Fikes and Nils J. Nilsson. 1971. STRIPS: A new approach to the application of theorem proving to problem solving. Artific. Intell. 2, 3 (1971), 189--208.Daniel Fišer, Michal Štolba, and Antonín Komenda. 2015. MAPlan. In Proceedings of the Competition of Distributed and Multi-Agent Planners (CoDMAP’15). 8--10.Foundation for Intelligent Physical Agents. 2002. FIPA Interaction Protocol Specification. Retrieved from http://www.fipa.org/repository/ips.php3.Maria Fox and Derek Long. 2003. PDDL2.1: An extension to PDDL for expressing temporal planning domains. J. Artific. Intell. Res. 20 (2003), 61--124.Malik Ghallab, Adele Howe, Craig Knoblock, Drew McDermott, Ashwin Ram, Manuela M. Veloso, Daniel Weld, and David Wilkins. 1998. PDDL—The planning domain definition language. AIPS-98 Planning Committee (1998).Malik Ghallab, Dana Nau, and Paolo Traverso. 2004. Automated Planning. Theory and Practice. Morgan Kaufmann.Barbara J. Grosz, Luke Hunsberger, and Sarit Kraus. 1999. Planning and acting together. AI Mag. 20, 4 (1999), 23--34.Malte Helmert. 2004. A planning heuristic based on causal graph analysis. Proceedings of the 14th International Conference on Automated Planning and Scheduling (ICAPS’04), 161--170.Malte Helmert. 2006. The fast downward planning system. J. Artific. Intell. Res. 26, 1 (2006), 191--246.Malte Helmert and Carmel Domshlak. 2009. Landmarks, critical paths and abstractions: What’s the difference anyway? In Proceedings of the 19th International Conference on Automated Planning and Scheduling (ICAPS’09). 162--169.Malte Helmert, Patrik Haslum, and Jörg Hoffmann. 2007. Flexible abstraction heuristics for optimal sequential planning. In Proceedings of the 17th International Conference on Automated Planning and Scheduling (ICAPS’07). 176--183.Jörg Hoffmann and Bernhard Nebel. 2001. The FF planning system: Fast planning generation through heuristic search. J. Artific. Intell. Res. 14 (2001), 253--302.Jörg Hoffmann, Julie Porteous, and Laura Sebastiá. 2004. Ordered landmarks in planning. J. Artific. Intell. Res. 22 (2004), 215--278.Jan Hrncír, Michael Rovatsos, and Michal Jakob. 2015. Ridesharing on timetabled transport services: A multiagent planning approach. J. Intell. Transportat. Syst. 19, 1 (2015), 89--105.Loïg Jezequel and Eric Fabre. 2012. A#: A distributed version of A* for factored planning. In Proceedings of the 51th IEEE Conference on Decision and Control (CDC’12). 7377--7382.Anders Jonsson and Michael Rovatsos. 2011. Scaling up multiagent planning: A best-response approach. In Proceedings of the 21st International Conference on Automated Planning and Scheduling (ICAPS’11). AAAI, 114--121.Jaume Jordán and Eva Onaindia. 2015. Game-theoretic approach for non-cooperative planning. In Proceedings of the 29th Conference on Artificial Intelligence (AAAI’15). 1357--1363.Froduald Kabanza, Lu Shuyun, and Scott Goodwin. 2004. Distributed hierarchical task planning on a network of clusters. In Proceedings of the 16th International Conference on Parallel and Distributed Computing and Systems (PDCS’04). 139--140.Henry A. Kautz. 2006. Deconstructing planning as satisfiability. In Proceedings of the National Conference on Artificial Intelligence, Vol. 21, 1524.Elena Kelareva, Olivier Buffet, Jinbo Huang, and Sylvie Thiébaux. 2007. Factored planning using decomposition trees. In Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI’07). 1942--1947.Antonín Komenda, Michal Stolba, and Daniel L. Kovacs. 2016. The international competition of distributed and multiagent planners (CoDMAP). AI Mag. 37, 3 (2016), 109--115.Daniel L. Kovacs. 2012. A multi-agent extension of PDDL3.1. In Proceedings of the 3rd Workshop on the International Planning Competition (IPC’12). 19--27.Jonas Kvarnström. 2011. Planning for loosely coupled agents using partial order forward-chaining. In Proceedings of the 21st International Conference on Automated Planning and Scheduling (ICAPS’11). AAAI, 138--145.Victor Lesser, Keith Decker, Thomas Wagner, Norman Carver, Alan Garvey, Bryan Horling, Daniel Neiman, Rodion Podorozhny, M. Nagendra Prasad, Anita Raja, Regis Vincent, Ping Xuan, and X. Q. Zhang. 2004. Evolution of the GPGP/TAEMS domain-independent coordination framework. Auton. Agents Multi-Agent Syst. 9, 1–2 (2004), 87--143.Derek Long, Henry Kautz, Bart Selman, Blai Bonet, Hector Geffner, Jana Koehler, Michael Brenner, Joerg Hoffmann, Frank Rittinger, Corin R. Anderson, Daniel S. Weld, David E. Smith, Maria Fox, and Derek Long. 2000. The AIPS-98 planning competition. AI Mag. 21, 2 (2000), 13--33.Nerea Luis and Daniel Borrajo. 2014. Plan merging by reuse for multi-agent planning. In Proceedings of the 2nd ICAPS Workshop on Distributed and Multi-Agent Planning (DMAP’14). 38--44.Nerea Luis and Daniel Borrajo. 2015. PMR: Plan merging by reuse. In Proceedings of the Competition of Distributed and Multi-Agent Planners (CoDMAP’15). 11--13.Shlomi Maliah, Ronen I. Brafman, and Guy Shani. 2017. Increased privacy with reduced communication in multi-agent planning. In Proceedings of the 27th International Conference on Automated Planning and Scheduling (ICAPS’17). 209--217.Shlomi Maliah, Guy Shani, and Roni Stern. 2014. Privacy preserving landmark detection. In Proceedings of the 21st European Conference on Artificial Intelligence (ECAI’14). 597--602.Shlomi Maliah, Guy Shani, and Roni Stern. 2016. Collaborative privacy preserving multi-agent planning. Auton. Agents Multi-Agent Syst. (2016), 1--38.Felipe Meneguzzi and Lavindra de Silva. 2015. Planning in BDI agents: A survey of the integration of planning algorithms and agent reasoning. Knowl. Eng. Rev. 30, 1 (2015), 1--44.Christian Muise, Nir Lipovetzky, and Miquel Ramirez. 2015. MAP-LAPKT: Omnipotent multi-agent planning via compilation to classical planning. In Proceedings of the Competition of Distributed and Multi-Agent Planners (CoDMAP’15). 14--16.Dana S. Nau, Tsz-Chiu Au, Okhtay Ilghami, Ugur Kuter, J. William Murdock, Dan Wu, and Fusun Yaman. 2003. SHOP2: An HTN planning system. J. Artific. Intell. Res. 20 (2003), 379--404.Raz Nissim and Ronen I. Brafman. 2012. Multi-agent A* for parallel and distributed systems. In Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems (AAMAS’12). 1265--1266.Raz Nissim and Ronen I. Brafman. 2013. Cost-optimal planning by self-interested agents. In Proceedings of the 27th Conference on Artificial Intelligence (AAAI’13).Raz Nissim and Ronen I. Brafman. 2014. Distributed heuristic forward search for multi-agent planning. J. Artific. Intell. Res. 51 (2014), 293--332.Raz Nissim, Ronen I. Brafman, and Carmel Domshlak. 2010. A general, fully distributed multi-agent planning algorithm. In Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems (AAMAS’10). 1323--1330.Sergio Pajares and Eva Onaindia. 2013. Context-aware multi-agent planning in intelligent environments. Info. Sciences 227 (2013), 22--42.Michal Pechoucek, Martin Rehák, Petr Charvát, Tomáš Vlcek, and Michal Kolar. 2007. Agent-based approach to mass-oriented production planning: Case study. IEEE Trans. Syst. Man Cybernet. Part C 37, 3 (2007), 386--395.Damien Pellier. 2010. Distributed planning through graph merging. In Proceedings of the 2nd International Conference on Agents and Artificial Intelligence (ICAART’10). 128--134.Miquel Ramirez, Nir Lipovetzky, and Christian Muise. 2015. Lightweight Automated Planning ToolKiT. Retrieved from http://lapkt.org/.Prashant P. Reddy and Manuela M. Veloso. 2011. Strategy learning for autonomous agents in smart grid markets. In Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI’11). 1446--1451.Silvia Richter and Matthias Westphal. 2010. The LAMA planner: Guiding cost-based anytime planning with landmarks. J. Artific. Intell. Res. 39, 1 (2010), 127--177.Valentin Robu, Han Noot, Han La Poutré, and Willem-Jan van Schijndel. 2011. A multi-agent platform for auction-based allocation of loads in transportation logistics. Expert Syst. Appl. 38, 4 (2011), 3483--3491.Óscar Sapena, Eva Onaindia, Antonio Garrido, and Marlene Arangú. 2008. A distributed CSP approach for collaborative planning systems. Eng. Appl. Artific. Intell. 21, 5 (2008), 698--709.Emilio Serrano, Jose M. Such, Juan A. Botía, and Ana García-Fornes. 2013. Strategies for avoiding preference profiling in agent-based e-commerce environments. Appl. Intell. (2013), 1--16.Sven Seuken and Shlomo Zilberstein. 2008. Formal models and algorithms for decentralized decision making under uncertainty. Auton. Agents Multi-Agent Syst. 17, 2 (2008), 190--250.Guy Shani, Shlomi Maliah, and Roni Stern. 2016. Stronger privacy preserving projections for multi-agent planning. In Proceedings of the 26th International Conference on Automated Planning and Scheduling (ICAPS’16). 221--229.Claude E. Shannon. 1948. A mathematical theory of communication. Bell Syst. Tech. J. 27, 3 (1948), 379--423.Evren Sirin, Bijan Parsia, Dan Wu, James Hendler, and Dana Nau. 2004. HTN planning for web service composition using SHOP2. J. Web Semant. 1, 4 (2004), 377--396.Sarath Sreedharan, Yu Zhang, and Subbarao Kambhampati. 2015. A first multi-agent planner for required cooperation (MARC). In Proceedings of the Competition of Distributed and Multi-Agent Planners (CoDMAP’15). 17--20.Michal Štolba, Daniel Fišer, and Antonín Komenda. 2015. Admissible landmark heuristic for multi-agent planning. In Proceedings of the 25th International Conference on Automated Planning and Scheduling (ICAPS’15). 211--219.Michal Štolba and Antonín Komenda. 2014. Relaxation heuristics for multiagent planning. In Proceedings of the 24th International Conference on Automated Planning and Scheduling (ICAPS’14). 298--306.Michal Štolba and Antonín Komenda. 2015. MADLA: Planning with distributed and local search. In Proceedings of the Competition of Distributed and Multi-Agent Planners (CoDMAP’15). 21--24.Michal Štolba, Jan Tožička, and Antonín Komenda. 2016. Quantifying privacy leakage in multi-agent planning. Proceedings of the 4rd ICAPS Workshop on Distributed and Multi-Agent Planning (DMAP’16). 80--88.Jose M. Such, Ana García-Fornes, Agustín Espinosa, and Joan Bellver. 2012. Magentix2: A privacy-enhancing agent platform. Eng. Appl. Artific. Intell. (2012), 96--109.Milind Tambe. 1997. Towards flexible teamwork. J. Artific. Intell. Res. 7 (1997), 83--124.Alejandro Torreño, Eva Onaindia, and Óscar Sapena. 2012. An approach to multi-agent planning with incomplete information. In Proceedings of the 20th European Conference on Artificial Intelligence (ECAI’12), Vol. 242. IOS Press, 762--767.Alejandro Torreño, Eva Onaindia, and Óscar Sapena. 2014. A flexible coupling approach to multi-agent planning under incomplete information. Knowl. Info. Syst. 38, 1 (2014), 141--178.Alejandro Torreño, Eva Onaindia, and Óscar Sapena. 2014. FMAP: Distributed cooperative multi-agent planning. Appl. Intell. 41, 2 (2014), 606--626.Alejandro Torreño, Eva Onaindia, and Óscar Sapena. 2015. Global heuristics for distributed cooperative multi-agent planning. In Proceedings of the 25th International Conference on Automated Planning and Scheduling (ICAPS’15). 225--233.Alejandro Torreño, Óscar Sapena, and Eva Onaindia. 2015. MH-FMAP: Alternating global heuristics in multi-agent planning. In Proceedings of the Competition of Distributed and Multi-Agent Planners (CoDMAP’15). 25--28.Jan Tožička, Jan Jakubuv, and Antonín Komenda. 2015. PSM-based planners description for CoDMAP 2015 competition. In Proceedings of the Competition of Distributed and Multi-Agent Planners (CoDMAP’15). 29--32.Jan Tožička, Jan Jakubuv, Antonín Komenda, and Michal Pěchouček. 2015. Privacy-concerned multiagent planning. Knowl. Info. Syst. 48, 3 (2016), 581–618.Jan Tožička, Michal Štolba, and Antonín Komenda. 2017. The limits of strong privacy preserving multi-agent planning. In Proceedings of the 27th International Conference on Automated Planning and Scheduling (ICAPS’17). 221--229.Roman van der Krogt. 2007. Privacy loss in classical multiagent planning. In Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT’07). 168--174.Roman van der Krogt. 2009. Quantifying privacy in multiagent planning. Multiagent Grid Syst. 5, 4 (2009), 451--469.David E. Wilkins. 1988. Practical Planning: Extending the Classical AI Planning Paradigm. Morgan Kaufmann.David E. Wilkins and Karen L. Myers. 1998. A multiagent planning architecture. In Proceedings of the 4th International Conference on Artificial Intelligence Planning Systems (AIPS’98). 154--162.Michael Wolverton and Marie desJardins. 1998. Controlling communication in distributed planning using irrelevance reasoning. In Proceedings of the 15th National Conference on Artificial Intelligence (AAAI’98). 868--874.Michael Wooldridge. 1997. Agent-based software engineering. IEEE Proc. Softw. Eng. 144, 1 (1997), 26--37.Yu Zhang and Subbarao Kambhampati. 2014. A formal analysis of required cooperation in multi-agent planning. CoRR abs/1404.5643 (2014). http://arxiv.org/abs/1404.5643