PDDL2.1: An extension of PDDL for expressing temporal planning domains

In recent years research in the planning community has moved increasingly towards application of planners to realistic problems involving both time and many types of resources. For example, interest in planning demonstrated by the space research community has inspired work in observation scheduling, planetary rover ex ploration and spacecraft control domains. Other temporal and resource-intensive domains including logistics planning, plant control and manufacturing have also helped to focus the community on the modelling and reasoning issues that must be confronted to make planning technology meet the challenges of application. The International Planning Competitions have acted as an important motivating force behind the progress that has been made in planning since 1998. The third competition (held in 2002) set the planning community the challenge of handling time and numeric resources. This necessitated the development of a modelling language capable of expressing temporal and numeric properties of planning domains. In this paper we describe the language, PDDL2.1, that was used in the competition. We describe the syntax of the language, its formal semantics and the validation of concurrent plans. We observe that PDDL2.1 has considerable modelling power --- exceeding the capabilities of current planning technology --- and presents a number of important challenges to the research community

Modelling Mixed Discrete-Continuous Domains for Planning

In this paper we present pddl+, a planning domain description language for modelling mixed discrete-continuous planning domains. We describe the syntax and modelling style of pddl+, showing that the language makes convenient the modelling of complex time-dependent effects. We provide a formal semantics for pddl+ by mapping planning instances into constructs of hybrid automata. Using the syntax of HAs as our semantic model we construct a semantic mapping to labelled transition systems to complete the formal interpretation of pddl+ planning instances. An advantage of building a mapping from pddl+ to HA theory is that it forms a bridge between the Planning and Real Time Systems research communities. One consequence is that we can expect to make use of some of the theoretical properties of HAs. For example, for a restricted class of HAs the Reachability problem (which is equivalent to Plan Existence) is decidable. pddl+ provides an alternative to the continuous durative action model of pddl2.1, adding a more flexible and robust model of time-dependent behaviour

PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains

In recent years research in the planning community has moved increasingly toward s application of planners to realistic problems involving both time and many typ es of resources. For example, interest in planning demonstrated by the space res earch community has inspired work in observation scheduling, planetary rover ex ploration and spacecraft control domains. Other temporal and resource-intensive domains including logistics planning, plant control and manufacturing have also helped to focus the community on the modelling and reasoning issues that must be confronted to make planning technology meet the challenges of application. The International Planning Competitions have acted as an important motivating fo rce behind the progress that has been made in planning since 1998. The third com petition (held in 2002) set the planning community the challenge of handling tim e and numeric resources. This necessitated the development of a modelling langua ge capable of expressing temporal and numeric properties of planning domains. In this paper we describe the language, PDDL2.1, that was used in the competition. We describe the syntax of the language, its formal semantics and the validation of concurrent plans. We observe that PDDL2.1 has considerable modelling power --- exceeding the capabilities of current planning technology --- and presents a number of important challenges to the research community

PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains

In recent years research in the planning community has moved increasingly toward s application of planners to realistic problems involving both time and many typ es of resources. For example, interest in planning demonstrated by the space res earch community has inspired work in observation scheduling, planetary rover ex ploration and spacecraft control domains. Other temporal and resource-intensive domains including logistics planning, plant control and manufacturing have also helped to focus the community on the modelling and reasoning issues that must be confronted to make planning technology meet the challenges of application. The International Planning Competitions have acted as an important motivating fo rce behind the progress that has been made in planning since 1998. The third com petition (held in 2002) set the planning community the challenge of handling tim e and numeric resources. This necessitated the development of a modelling langua ge capable of expressing temporal and numeric properties of planning domains. In this paper we describe the language, PDDL2.1, that was used in the competition. We describe the syntax of the language, its formal semantics and the validation of concurrent plans. We observe that PDDL2.1 has considerable modelling power --- exceeding the capabilities of current planning technology --- and presents a number of important challenges to the research community

Optimal Planning Modulo Theories

Planning for real-world applications requires algorithms and tools with the ability to handle the complexity such scenarios entail. However, meeting the needs of such applications poses substantial challenges, both representational and algorithmic. On the one hand, expressive languages are needed to build faithful models. On the other hand, efficient solving techniques that can support these languages need to be devised. A response to this challenge is underway, and the past few years witnessed a community effort towards more expressive languages, including decidable fragments of first-order theories. In this work we focus on planning with arithmetic theories and propose Optimal Planning Modulo Theories, a framework that attempts to provide efficient means of dealing with such problems. Leveraging generic Optimization Modulo Theories (OMT) solvers, we first present domain-specific encodings for optimal planning in complex logistic domains. We then present a more general, domain- independent formulation that allows to extend OMT planning to a broader class of well-studied numeric problems in planning. To the best of our knowledge, this is the first time OMT procedures are employed in domain-independent planning

Inference and Learning with Planning Models

[ES] Inferencia y aprendizaje son los actos de razonar sobre evidencia recogida con el fin de alcanzar conclusiones lógicas sobre el proceso que la originó. En el contexto de un modelo de espacio de estados, inferencia y aprendizaje se refieren normalmente a explicar el comportamiento pasado de un agente, predecir sus acciones futuras, o identificar su modelo. En esta tesis, presentamos un marco para inferencia y aprendizaje en el modelo de espacio de estados subyacente al modelo de planificación clásica, y formulamos una paleta de problemas de inferencia y aprendizaje bajo este paraguas unificador. También desarrollamos métodos efectivos basados en planificación que nos permiten resolver estos problemas utilizando algoritmos de planificación genéricos del estado del arte. Mostraremos que un gran número de problemas de inferencia y aprendizaje claves que han sido tratados como desconectados se pueden formular de forma cohesiva y resolver siguiendo procedimientos homogéneos usando nuestro marco. Además, nuestro trabajo abre las puertas a nuevas aplicaciones para tecnología de planificación ya que resalta las características que hacen que el modelo de espacio de estados de planificación clásica sea diferente a los demás modelos.[CA] Inferència i aprenentatge són els actes de raonar sobre evidència arreplegada a fi d'aconseguir conclusions lògiques sobre el procés que la va originar. En el context d'un model d'espai d'estats, inferència i aprenentatge es referixen normalment a explicar el comportament passat d'un agent, predir les seues accions futures, o identificar el seu model. En esta tesi, presentem un marc per a inferència i aprenentatge en el model d'espai d'estats subjacent al model de planificació clàssica, i formulem una paleta de problemes d'inferència i aprenentatge davall este paraigua unificador. També desenrotllem mètodes efectius basats en planificació que ens permeten resoldre estos problemes utilitzant algoritmes de planificació genèrics de l'estat de l'art. Mostrarem que un gran nombre de problemes d'inferència i aprenentatge claus que han sigut tractats com desconnectats es poden formular de forma cohesiva i resoldre seguint procediments homogenis usant el nostre marc. A més, el nostre treball obri les portes a noves aplicacions per a tecnologia de planificació ja que ressalta les característiques que fan que el model d'espai d'estats de planificació clàssica siga diferent dels altres models.[EN] Inference and learning are the acts of reasoning about some collected evidence in order to reach a logical conclusion regarding the process that originated it. In the context of a state-space model, inference and learning are usually concerned with explaining an agent's past behaviour, predicting its future actions or identifying its model. In this thesis, we present a framework for inference and learning in the state-space model underlying the classical planning model, and formulate a palette of inference and learning problems under this unifying umbrella. We also develop effective planning-based approaches to solve these problems using off-the-shelf, state-of-the-art planning algorithms. We will show that several core inference and learning problems that previous research has treated as disconnected can be formulated in a cohesive way and solved following homogeneous procedures using the proposed framework. Further, our work opens the way for new applications of planning technology as it highlights the features that make the state-space model of classical planning different from other models.The work developed in this doctoral thesis has been possible thanks to the FPU16/03184 fellowship that I have enjoyed for the duration of my PhD studies. I have also been supported by my advisors’ grants TIN2017-88476-C2-1-R, TIN2014-55637-C2-2-R-AR, and RYC-2015-18009.Aineto García, D. (2022). Inference and Learning with Planning Models [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/18535

Automated Planning with Hybrid Domain Models: A Method to Improve Continuous Process Descriptions

Recent advances in Automated Planning not only involve the improvements in planning efficiency but also the enhancement in granularity in which planning domains are modelled. A significant progression is in the move from discrete domain models to mixed discrete-continuous models i.e. hybrid domain models. While planning with hybrid domains has been studied for decades, the knowledge engineering of those domain models is still a challenge, particularly for real-time complex domains. It is imperative to understand how to effectively and efficiently formulate the planning models to achieve maximum productivity with minimum wasted effort or cost. One of the main engineering challenges of hybrid domain models involves encoding the frequent fluctuation of underlying processes with continuous updates in the world state. The occurring numerical changes with the variation of parameters can be too complex to be formulated accurately by human manual efforts. This thesis proposes a method utilising machine learning techniques which results in the formulation of a run-time representative estimation of continuous changes in varied process parameters. The method incorporates statistical analysis to acquire process models from real-world data for hybrid planning domains. We assume that domain knowledge has been already encoded in an initial hybrid domain model (in this thesis, that is a PDDL+ model). We then use the method to create an improved process model (within the same encoding language of PDDL+) which is embedded into the process specification of the original, pre-engineered domain model. By exploiting the quantitative data from hybrid planning domains, firstly the proposed approach, with the help of statistical methods, identifies the associations (i.e. dependencies or inter-dependencies) between a single outcome and single/multiple predictor numeric variables in the underlying process. Based on the deduced statistical relationship among variables, the appropriate linear regression technique with corresponding statistical tests are nominated and implemented to formulate the process model. Then the constructed process model is embedded/adjusted into the pre-engineered hybrid domain models. The learned process model, in the form of a mathematical function, automatically approximates/adjusts the quantity of an outcome variable with the continuous variations in different predictor features in order to efficiently and accurately control the corresponding process in hybrid planning domains. To empirically evaluate our approach, we utilise pre-engineered models (in PDDL+) of an Urban Traffic Control (UTC) domain and a Coffee domain. For the UTC domain, we experiment with the real-time traffic data that is collected from AIMSUN simulator utilised in the SimplifAI project (McCluskey, Vallati and Franco 2017). Besides, for coffee domain, we collect the real-time data from an observational study that is conducted by Easthope (2015). The evaluation results demonstrate that the automatically learned values of numeric process variables by our method are more rational than the formulation values of process variables declared statically in the original domain models. Besides, it reveals that the learned process models can provide more accurate simulation output, which can consequently lead to higher-quality plans. Along with that, by automatically identifying the effective process variables and removing the irrelevant ones from the learned process models, it can assist the knowledge engineering tasks of modelling/adjusting the dynamically changing process variables with their values in the hybrid planning domains, without declaring them statically