Learning Hierarchical Task Networks Using Semantic Word Embeddings

This thesis describes WORD2HTN, which is a novel and semantic approach for learning hierarchical task networks (HTN) and semantic division of goals from input plan traces. The semantic relationships are learned using machine learning to get the vector representations of the components of the plan trace. The semantic relationships are used to learn hierarchical landmarks, which in turn are used to make semantically divided HTNs. These learned HTNs can then be used for subsequent new problems in the domain that have a similar structure with the problems in the input plan traces. This work also improves the learning algorithm to include arithmetic conditions and effects. WORD2HTN was tested on 3 deterministic domains. These are Logistics or Transportation domain, Abstract Graph domain, and the Malmo interface for the Minecraft game. We show that WORD2HTN learns semantically divided HTNs. We also experimentally demonstrate that HTN planners using this have an exponential speedup in information-dense domains over the state of the art classical planner. Finally, we show that the HTNs learned in Minecraft can be used to achieve tasks faster with a cooperative agent controlled by the HTN planner’s output

Hierarchical Task Recognition and Planning in Smart Homes with Partial Observability

Older adults with cognitive impairment have significantly burdened their families and the society due to costly caring and waste of labors. Developing intelligent assistant agents (IAAs) in smart homes that can help those people accomplishing activities of daily living (ADLs) independently has attracted tremendous attention, from both academia and industry. Ideally, IAAs should recognize older adults’ goals and reason about further steps needed for the goals. This paper proposed a goal recognition and planning algorithm to support an IAA in smart home. The algorithm addresses several important issues. First it can deal with partial observability by Bayesian inference for step recognition. Even advanced sensors are not guaranteed to be 100% reliable. Besides, due to limited accessibility or privacy, not all attributes of physical objects can be measured by sensors. The proposed algorithm can reason about ongoing goals with some sensors missing or unreliable. Second, the algorithm reasons about concurrent goals. For everyday life, a person is typically involved in multi-tasks by switching back and forth. Based on the context, the proposed algorithm can assign a step to the correct goal and keep tracks of the goal’s ongoing status. The context involves status of ongoing goals inferred from a recognition procedure, and desired next steps and tasks, which are obtained through a planning procedure. Last but not least, the algorithm can handle incorrectly executed steps. For older adults with cognitive impairment, executing unrelated or wrong steps towards certain goals is common in their daily life. A module is designed to hand wrong steps by detecting and then prompt the person with correct steps. The algorithm is based on Hierarchical Task Network (HTN), of which the knowledge base is composed of methods (for tasks) and operators (for steps). Such hierarchical modeling of tasks and steps enables the algorithm to deal with partially ordered subtasks and alternative plans. Furthermore, the preconditions of methods and operators enable to generate feasible hints of next steps and tasks by considering uncertainties in belief states. In the experiment, a simulator is designed to simulate the virtual sensors and a virtual human executing a sequence of steps predefined in a test case. The algorithm is tested on many simulated easy or difficult cases. For example single goal and correct steps are easy test cases. Having multiple goals with wrong steps makes the problem more difficult. Also cases of sensors missing are experimented. The results shows that the algorithm works very well on simple cases, achieving nearly 100% accuracy. Even for the hardest cases, the performance is acceptable when sensor reliabilities are above 0.95. Test cases with missing sensors also provide meaningful guideline for setting up sensors for an intelligent assistant agent

Learning teleoreactive logic programs from problem solving

Abstract. In this paper, we focus on the problem of learning reactive skills for use by physical agents. We propose a new representation for such procedures, teleoreactive logic programs, along with an interpreter that utilizes them to achieve goals. After this, we describe a learning method that acquires these structures in a cumulative manner through problem solving. We report experiments in three domains that involve multiple levels of skilled behavior. We also review related work and discuss directions for future research.

Desarrollo automatizado de sistemas teleo-reactivos a partir de objetivos: un enfoque basado en componentes y dirigido por modelos

[SPA] Esta tesis doctoral se presenta bajo la modalidad de compendio de publicaciones. Está formada por un total de cuatro artículos publicados en revistas del segundo cuartil del Journal Citation Reports. El artículo “A systematic literature review of the Teleo-Reactive paradigm” ofrece una completa revisión sistemática de la literatura existente sobre el paradigma Teleo-Reactivo desde su presentación por el profesor Nils Nilsson en el año 1994. Su papel en esta tesis es el de servir de estado del arte de dicho paradigma, ofreciendo una buena perspectiva de la evolución de los sistemas Teleo-Reactivos desde su formulación hasta el presente. Para poder desarrollar sistemas Teleo-Reactivos a partir de objetivos, surgió la necesidad de especificar los requisitos de estos sistemas usando el lenguaje más apropiado. Ese es uno de los objetivos principales del artículo “A controlled experiment to evaluate the understandability of KAOS and i* for modeling Teleo-Reactive systems”. Como resultado de dicho trabajo se decidió utilizar i* dado que el experimento realizado mostró que las especificaciones realizadas con dicho lenguaje resultaban ligeramente más comprensibles que las realizadas con KAOS. Aunque i* resultaba más comprensible a la hora de especificar requisitos para sistemas Teleo-Reactivos, también presentaba ciertas debilidades. Estas debilidades han sido descritas detalladamente en el artículo “A family of experiments to evaluate the understandability of TRiStar and i* for modeling Teleo-Reactive systems”, en el que además se propone una extensión al lenguaje que permite superarlas. La extensión propuesta se denomina TRiStar y fue inicialmente presentada en [Morales15]. TRiStar ha demostrado superar los problemas de comprensibilidad identificados en i* en el modelado de sistemas Teleo-Reactivos mediante una familia de experimentos realizada con estudiantes de últimos cursos de grado y con desarrolladores software experimentados, cuyos resultados se exponen exhaustivamente en el artículo mencionado. En él se describe, además, un mecanismo que permite obtener mediante transformación de modelos el programa Teleo-Reactivo equivalente a un diagrama TRiStar dado. TRiStar permite, por lo tanto, partiendo de los objetivos de un sistema Teleo-Reactivo obtener un diagrama que especifique su comportamiento. Ese diagrama puede ser transformado en un programa Teleo-Reactivo equivalente. Y siguiendo las transformaciones descritas en “From Teleo-Reactive specifications to architectural components: a model-driven approach” se puede obtener a partir del programa Teleo-Reactivo el modelo de componentes y la máquina de estados que describe el comportamiento de cada uno de esos componentes. Con estos elementos y usando un framework como el descrito en [Iborra09] se cerraría el proceso de desarrollo del sistema Teleo-Reactivo. Como resultado de las investigaciones realizadas en el transcurso de esta tesis, y aunque no forma parte del compendio, hay un quinto artículo [Sánchez16] que está en segunda revisión en el Journal of Systems and Software en el que se estudian las posibilidades de introducir requisitos de tiempo real cuando se sigue el enfoque Teleo-Reactivo desde el modelado a la implementación de un sistema. Tras realizar un estudio del tipo de restricciones temporales que se pueden imponer desde el punto de vista Teleo-Reactivo, se considera la posibilidad de utilizar TeleoR [Clark14] para incorporar dichas restricciones y se proponene una serie de extensiones a TRiStar para permitir representar requisitos temporales. Estas extensiones dan lugar a lo que hemos llamado TRiStar+. [ENG] This doctoral dissertation has been presented in the form of thesis by publication. It is comprised of four articles indexed in the second quartile of the Journal Citation Reports. The article “A systematic literature review of the Teleo-Reactive paradigm” offers a complete systematic review of the existing literature on the Teleo-Reactive paradigm since Prof. Nils Nilsson presented it in 1994. It plays the role of state of the art of that paradigm, showing a perspective of the evolution of Teleo-Reactive systems from their formulation to present time. In order to develop Teleo-Reactive systems starting from its goals, there is the need of specifying the requirements of these systems using the most adequate language. That is one of the main objectives of the article “A controlled experiment to evaluate the understandability of KAOS and i* for modeling Teleo-Reactive systems”. As a result, we decided to use i* because the experiment showed that i* specifications where slightly more understandable than those made using KAOS. Although i* was more understandable when specifying requirements for Teleo-Reactive systems, the experiment also showed some shortcomings. These shortcomings have been deeply described in the article “A family of experiments to evaluate the understandability of TRiStar and i* for modeling Teleo-Reactive systems”. In this article, an extension to i* is proposed in order to overcome the identified limitations. The proposed extension is named TRiStar and was initially presented at [Morales15]. TRiStar has shown to be more understandable than i* when modeling Teleo-Reactive systems through a family of experiments done with last year students and experienced software developers, whose results are described in the aforementioned article. In that article, a mechanism to obtain a Teleo-Reactive program starting from a TRiStar diagram is also described. Therefore, TRiStar allows obtaining a diagram which specifies the behavior of a Teleo-Reactive system starting from its goals. That diagram can be transformed into an equivalent Teleo-Reactive program. Then, following the transformations described in “From Teleo-Reactive specifications to architectural components: a model-driven approach”, a component model and the state machine describing the behavior of each of those components can be obtained. With these elements and using a framework as that described in [Iborra09], the development process of the Teleo-Reactive system would be finished. As a result of the research carried out during this dissertation there is another article, which is not comprised in the compilation, in second revision at the Journal of Systems and Software [Sánchez16]. In that article, after making a study of the type of timing constraints from the TR perspective, we consider the possibility of using TeleoR [Clark14] for incorporating such constraints. Some extensions on TRiStar notation are proposed to represent temporal requirements. Those extensions have been named TRiStar+.[ENG] This doctoral dissertation has been presented in the form of thesis by publication. It is comprised of four articles indexed in the second quartile of the Journal Citation Reports. The article “A systematic literature review of the Teleo-Reactive paradigm” offers a complete systematic review of the existing literature on the Teleo-Reactive paradigm since Prof. Nils Nilsson presented it in 1994. It plays the role of state of the art of that paradigm, showing a perspective of the evolution of Teleo-Reactive systems from their formulation to present time. In order to develop Teleo-Reactive systems starting from its goals, there is the need of specifying the requirements of these systems using the most adequate language. That is one of the main objectives of the article “A controlled experiment to evaluate the understandability of KAOS and i* for modeling Teleo-Reactive systems”. As a result, we decided to use i* because the experiment showed that i* specifications where slightly more understandable than those made using KAOS. Although i* was more understandable when specifying requirements for Teleo-Reactive systems, the experiment also showed some shortcomings. These shortcomings have been deeply described in the article “A family of experiments to evaluate the understandability of TRiStar and i* for modeling Teleo-Reactive systems”. In this article, an extension to i* is proposed in order to overcome the identified limitations. The proposed extension is named TRiStar and was initially presented at [Morales15]. TRiStar has shown to be more understandable than i* when modeling Teleo-Reactive systems through a family of experiments done with last year students and experienced software developers, whose results are described in the aforementioned article. In that article, a mechanism to obtain a Teleo-Reactive program starting from a TRiStar diagram is also described. Therefore, TRiStar allows obtaining a diagram which specifies the behavior of a Teleo-Reactive system starting from its goals. That diagram can be transformed into an equivalent Teleo-Reactive program. Then, following the transformations described in “From Teleo-Reactive specifications to architectural components: a model-driven approach”, a component model and the state machine describing the behavior of each of those components can be obtained. With these elements and using a framework as that described in [Iborra09], the development process of the Teleo-Reactive system would be finished. As a result of the research carried out during this dissertation there is another article, which is not comprised in the compilation, in second revision at the Journal of Systems and Software [Sánchez16]. In that article, after making a study of the type of timing constraints from the TR perspective, we consider the possibility of using TeleoR [Clark14] for incorporating such constraints. Some extensions on TRiStar notation are proposed to represent temporal requirements. Those extensions have been named TRiStar+.Universidad Politécnica de CartagenaPrograma Oficial de Doctorado en Tecnologías de la Información y Comunicacione