ICAPS 2012. Proceedings of the third Workshop on the International Planning Competition

22nd International Conference on Automated Planning and Scheduling. June 25-29, 2012, Atibaia, Sao Paulo (Brazil). Proceedings of the 3rd the International Planning CompetitionThe Academic Advising Planning Domain / Joshua T. Guerin, Josiah P. Hanna, Libby Ferland, Nicholas Mattei, and Judy Goldsmith. -- Leveraging Classical Planners through Translations / Ronen I. Brafman, Guy Shani, and Ran Taig. -- Advances in BDD Search: Filtering, Partitioning, and Bidirectionally Blind / Stefan Edelkamp, Peter Kissmann, and Álvaro Torralba. -- A Multi-Agent Extension of PDDL3.1 / Daniel L. Kovacs. -- Mining IPC-2011 Results / Isabel Cenamor, Tomás de la Rosa, and Fernando Fernández. -- How Good is the Performance of the Best Portfolio in IPC-2011? / Sergio Nuñez, Daniel Borrajo, and Carlos Linares López. -- “Type Problem in Domain Description!” or, Outsiders’ Suggestions for PDDL Improvement / Robert P. Goldman and Peter KellerEn prens

Dynamic Web Services Composition

Emerging web services technology has introduced the concept of autonomic interoperability and portability between services. The number of online services has increased dramatically with many duplicating similar functionality and results. Composing online services to solve user needs is a growing area of research. This entails designing systems which can discover participating services and integrate these according to the end user requirements. This thesis proposes a Dynamic Web Services Composition (DWSC) process that is based upon consideration of previously successful attempts in this area, in particular utilizing AI-planning based solutions. It proposes a unique approach for service selection and dynamic web service composition by exploring the possibility of semantic web usability and its limitations. It also proposes a design architecture called Optimal Synthesis Plan Generation framework (OSPG), which supports the composition process through the evaluation of all available solutions (including all participating single and composite services). OSPG is designed to take into account user preferences, which supports optimality and robustness of the output plan. The implementation of OSPG will be con�gured and tested via division of search criteria in di�erent modes thereby locating the best plan for the user. The services composition and discovery-based model is evaluated via considering a range of criteria, such as scope, correctness, scalability and versatility metrics

AMPLE: an anytime planning and execution framework for dynamic and uncertain problems in robotics

Acting in robotics is driven by reactive and deliberative reasonings which take place in the competition between execution and planning processes. Properly balancing reactivity and deliberation is still an open question for harmonious execution of deliberative plans in complex robotic applications. We propose a flexible algorithmic framework to allow continuous real-time planning of complex tasks in parallel of their executions. Our framework, named AMPLE, is oriented towards robotic modular architectures in the sense that it turns planning algorithms into services that must be generic, reactive, and valuable. Services are optimized actions that are delivered at precise time points following requests from other modules that include states and dates at which actions are needed. To this end, our framework is divided in two concurrent processes: a planning thread which receives planning requests and delegates action selection to embedded planning softwares in compliance with the queue of internal requests, and an execution thread which orchestrates these planning requests as well as action execution and state monitoring. We show how the behavior of the execution thread can be parametrized to achieve various strategies which can differ, for instance, depending on the distribution of internal planning requests over possible future execution states in anticipation of the uncertain evolution of the system, or over different underlying planners to take several levels into account. We demonstrate the flexibility and the relevance of our framework on various robotic benchmarks and real experiments that involve complex planning problems of different natures which could not be properly tackled by existing dedicated planning approaches which rely on the standard plan-then-execute loop

Planning and learning under uncertainty

Automated Planning is the component of Artificial Intelligence that studies the computational process of synthesizing sets of actions whose execution achieves some given objectives. Research on Automated Planning has traditionally focused on solving theoretical problems in controlled environments. In such environments both, the current state of the environment and the outcome of actions, are completely known. The development of real planning applications during the last decade (planning fire extinction operations (Castillo et al., 2006), planning spacecraft activities (Nayak et al., 1999), planning emergency evacuation actions (Muñoz-Avila et al., 1999) has evidenced that these two assumptions are not true in many real-world problems. The planning research community is aware of this issue and during the last years, it has multiply its efforts to find new planning systems able to address these kinds of problems. All these efforts have created a new field in Automated Planning called planning under uncertainty. Nevertheless, the new systems suffer from two limitations. (1) They precise accurate action models, though the definition by hand of accurate action models is frequently very complex. (2) They present scalability problems due to the combinatorial explosion implied by the expressiveness of its action models. This thesis defines a new planning paradigm for building, in an efficient and scalable way, robust plans in domains with uncertainty though the action model is incomplete. The thesis is that, the integration of relational machine learning techniques with the planning and execution processes, allows to develop planning systems that automatically enrich their initial knowledge about the environment and therefore find more robust plans. An empirical evaluation illustrates these benefits in comparison with state-of-the-art probabilistic planners which use static actions models. -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------La Planificación Automática es la rama de la Inteligencia Artificial que estudia los procesos computacionales para la síntesis de conjuntos de acciones cuya ejecución permita alcanzar unos objetivos dados. Históricamente, la investigación en esta rama ha tratado de resolver problemas teóricos en entornos controlados en los que conocía tanto el estado actual del entorno como el resultado de ejecutar acciones en él. En la última década, el desarrollo de aplicaciones de planificación (gestión de las tareas de extinción de incendios forestales (Castillo et al., 2006), control de las actividades de la nave espacial Deep Space 1 (Nayak et al., 1999), planificación de evacuaciones de emergencia (Muñoz-Avila et al., 1999) ha evidenciado que tales supuestos no son ciertos en muchos problemas reales. Consciente de ello, la comunidad investigadora ha multiplicado sus esfuerzos para encontrar nuevos paradigmas de planificación que se ajusten mejor a este tipo de problemas. Estos esfuerzos han llevado al nacimiento de una nueva área dentro de la Planificación Automática, llamada planificación con incertidumbre. Sin embargo, los nuevos planificadores para dominios con incertidumbre aún presentan dos importantes limitaciones: (1) Necesitan modelos de acciones detallados que contemplen los posibles resultados de ejecutar cada acción. En la mayoría de problemas reales es difícil obtener modelos de este tipo. (2) Presentan fuertes problemas de escalabilidad debido a la explosión combinatoria que provoca la complejidad de los modelos de acciones que manejan. En esta Tesis se define un paradigma de planificación capaz de generar, de forma eficiente y escalable, planes robustos en dominios con incertidumbre aunque no se disponga de modelos de acciones completamente detallados. La Tesis que se defiende es que la integración de técnicas de aprendizaje automático relacional con los procesos de decisión y ejecución permite desarrollar sistemas de planificación capaces de enriquecer automáticamente su modelo de acciones con información adicional que les ayuda a encontrar planes más robustos. Los beneficios de esta integración son evaluados experimentalmente mediante una comparación con planificadores probabilísticos del estado del arte los cuales no modifican su modelo de acciones

Interfaces, Standards and Code lists in Data communication between Authorities

The use of harmonised data contents and common terminology, in particular when exchanging data between authority information systems, will result in smooth transport flow and less delays in border crossing due to more effective data communication with less errors, ambiguities and confusion. The aim of this project was to provide information and ideas on how data exchange among authorities and between authorities and business could be made more effective and harmonised. Harmonoitujen tietosisältöjen ja yhtenäisen terminologian käyttö erityisesti viranomaisten välisessä tiedonsiirrossa ja tietojärjestelmissä edistää liikennevirran sujuvuutta ja viiveiden vähentymistä rajanylityksessä, tehokkaamman tiedonsiirron sekä virheiden, epäselvyyksien ja sekaannuksien vähenemisen seurauksena.Hankkeen tavoitteena oli tuottaa tietoa ja suosituksia siitä, miten tietojen vaihtoa viranomaisten kesken sekä viranomaisten ja yritysten välillä voitaisiin tehostaa ja yhdenmukaistaa. Raportti on englanninkielinen

Foundations of Human-Aware Planning -- A Tale of Three Models

abstract: A critical challenge in the design of AI systems that operate with humans in the loop is to be able to model the intentions and capabilities of the humans, as well as their beliefs and expectations of the AI system itself. This allows the AI system to be "human- aware" -- i.e. the human task model enables it to envisage desired roles of the human in joint action, while the human mental model allows it to anticipate how its own actions are perceived from the point of view of the human. In my research, I explore how these concepts of human-awareness manifest themselves in the scope of planning or sequential decision making with humans in the loop. To this end, I will show (1) how the AI agent can leverage the human task model to generate symbiotic behavior; and (2) how the introduction of the human mental model in the deliberative process of the AI agent allows it to generate explanations for a plan or resort to explicable plans when explanations are not desired. The latter is in addition to traditional notions of human-aware planning which typically use the human task model alone and thus enables a new suite of capabilities of a human-aware AI agent. Finally, I will explore how the AI agent can leverage emerging mixed-reality interfaces to realize effective channels of communication with the human in the loop.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Design and implementation of a Multi-Agent Planning System

This work introduces the design and implementation of a Multi-Agent Planning framework, in which a set of agents work jointly in order to devise a course of action to solve a certain planning problem.Torreño Lerma, A. (2011). Design and implementation of a Multi-Agent Planning System. http://hdl.handle.net/10251/15358Archivo delegad