Service composition in stochastic settings

With the growth of the Internet-of-Things and online Web services, more services with more capabilities are available to us. The ability to generate new, more useful services from existing ones has been the focus of much research for over a decade. The goal is, given a specification of the behavior of the target service, to build a controller, known as an orchestrator, that uses existing services to satisfy the requirements of the target service. The model of services and requirements used in most work is that of a finite state machine. This implies that the specification can either be satisfied or not, with no middle ground. This is a major drawback, since often an exact solution cannot be obtained. In this paper we study a simple stochastic model for service composition: we annotate the tar- get service with probabilities describing the likelihood of requesting each action in a state, and rewards for being able to execute actions. We show how to solve the resulting problem by solving a certain Markov Decision Process (MDP) derived from the service and requirement specifications. The solution to this MDP induces an orchestrator that coincides with the exact solution if a composition exists. Otherwise it provides an approximate solution that maximizes the expected sum of values of user requests that can be serviced. The model studied although simple shades light on composition in stochastic settings and indeed we discuss several possible extensions

Temporal reasoning in a logic programming language with modularity

Actualmente os Sistemas de Informação Organizacionais (SIO) lidam cada vez mais com informação que tem dependências temporais. Neste trabalho concebemos um ambiente de trabalho para construir e manter SIO Temporais. Este ambiente assenta sobre um linguagem lógica denominada Temporal Contextua) Logic Programming que integra modularidade com raciocínio temporal fazendo com que a utilização de um módulo dependa do tempo do contexto. Esta linguagem é a evolução de uma outra, também introduzida nesta tese, que combina Contextua) Logic Programming com Temporal Annotated Constraint Logic Programming, na qual a modularidade e o tempo são características ortogonais. Ambas as linguagens são formalmente discutidas e exemplificadas. As principais contribuições do trabalho descrito nesta tese incluem: • Optimização de Contextua) Logic Programming (CxLP) através de interpretação abstracta. • Sintaxe e semântica operacional para uma linguagem que combina de um modo independente as linguagens Temporal Annotated Constraint Logic Programming (TACLP) e CxLP. É apresentado um compilador para esta linguagem. • Linguagem (sintaxe e semântica) que integra de um modo inovador modularidade (CxLP) com raciocínio temporal (TACLP). Nesta linguagem a utilização de um dado módulo está dependente do tempo do contexto. É descrito um interpretador e um compilador para esta linguagem. • Ambiente de trabalho para construir e fazer a manutenção de SIO Temporais. Assenta sobre uma especificação revista da linguagem ISCO, adicionando classes e manipulação de dados temporais. É fornecido um compilador em que a linguagem resultante é a descrita no item anterior. ABSTRACT- Current Organisational Information Systems (OIS) deal with more and more Infor-mation that, is time dependent. In this work we provide a framework to construct and maintain Temporal OIS. This framework builds upon a logical language called Temporal Contextual. Logic Programming that deeply integrates modularity with tem-poral reasoning making the usage of a module time dependent. This language is an evolution of another one, also introduced in this thesis, that combines Contextual Logic Programming with Temporal Annotated Constraint Logic Programming where modularity and time are orthogonal features. Both languages are formally discussed and illustrated. The main contributions of the work described in this thesis include: • Optimisation of Contextual Logic Programming (CxLP) through abstract interpretation. • Syntax and operational semantics for an independent combination of the temporal framework Temporal Annotated Constraint Logic Programming (TACLP) and CxLP. A compiler for this language is also provided. • Language (syntax and semantics) that integrates in a innovative way modularity (CxLP) with temporal reasoning (TACLP). In this language the usage of a given module depends of the time of the context. An interpreter and a compiler for this language are described. • Framework to construct and maintain Temporal Organisational Information Systems. It builds upon a revised specification of the language ISCO, adding temporal classes and temporal data manipulation. A compiler targeting the language presented in the previous item is also given

Reasoning about Actions with Temporal Answer Sets

In this paper we combine Answer Set Programming (ASP) with Dynamic Linear Time Temporal Logic (DLTL) to define a temporal logic programming language for reasoning about complex actions and infinite computations. DLTL extends propositional temporal logic of linear time with regular programs of propositional dynamic logic, which are used for indexing temporal modalities. The action language allows general DLTL formulas to be included in domain descriptions to constrain the space of possible extensions. We introduce a notion of Temporal Answer Set for domain descriptions, based on the usual notion of Answer Set. Also, we provide a translation of domain descriptions into standard ASP and we use Bounded Model Checking techniques for the verification of DLTL constraints.Comment: To appear in Theory and Practice of Logic Programmin

Konsistente Feature Modell gesteuerte Softwareproduktlinien Evolution

SPLs are an approach to manage families of closely related software systems in terms of configurable functionality. A feature model captures common and variable functionalities of an SPL on a conceptual level in terms of features. Reusable artifacts, such as code, documentation, or tests are related to features using a feature-artifact mapping. A product of an SPL can be derived by selecting features in a configuration. Over the course of time, SPLs and their artifacts are subject to change. As SPLs are particularly complex, their evolution is a challenging task. Consequently, SPL evolution must be thoroughly planned well in advance. However, plans typically do not turn out as expected and, thus, replanning is required. Feature models lean themselves for driving SPL evolution. However, replanning of feature-model evolution can lead to inconsistencies and feature-model anomalies may be introduced during evolution. Along with feature-model evolution, other SPL artifacts, especially configurations, need to consistently evolve. The work of this thesis provides remedy to the aforementioned challenges by presenting an approach for consistent evolution of SPLs. The main contributions of this thesis can be distinguished into three key areas: planning and replanning feature-model evolution, analyzing feature-model evolution, and consistent SPL artifact evolution. As a starting point for SPL evolution, we introduce Temporal Feature Models (TFMs) that allow capturing the entire evolution timeline of a feature model in one artifact, i.e., past history, present changes, and planned evolution steps. We provide an execution semantics of feature-model evolution operations that guarantees consistency of feature-model evolution timelines. To keep feature models free from anomalies, we introduce analyses to detect anomalies in feature-model evolution timelines and explain these anomalies in terms of their causing evolution operations. To enable consistent SPL artifact evolution, we generalize the concept of modeling evolution timelines in TFMs to be applicable for any modeling language. Moreover, we provide a methodology that enables involved engineers to define and use guidance for configuration evolution.Softwareproduktlinien (SPLs) ermöglichen es, konfigurierbare Funktionalität von eng verwandten Softwaresystemen zu verwalten. In einem Feature Modell werden gemeinsame und variable Funktionalitäten einer SPL auf Basis abstrakter Features modelliert. Wiederverwendbare Artefakte werden in einem Feature-Artefakt Mapping Features zugeordnet. Ein Produkt einer SPL kann abgeleitet werden, indem Features in einer Konfiguration ausgewählt werden. Im Laufe der Zeit müssen sich SPLs und deren Artefakte verändern. Da SPLs ganze Softwarefamilien modellieren, ist deren Evolution eine besonders herausfordernde Aufgabe, die gründlich im Voraus geplant werden muss. Feature Modelle eignen sich besonders als Planungsmittel einer SPL. Umplanung von Feature Modell Evolution kann jedoch zu Inkonsistenzen führen und Feature Modell Anomalien können im Zuge der Evolution eingeführt werden. Im Anschluss an die Feature Modell Evolution muss die Evolution anderer SPL Artefakte, insbesondere Konfigurationen, konsistent modelliert werden. In dieser Arbeit wird ein Ansatz zur konsistenten Evolution von SPLs vorgestellt, der die zuvor genannten Herausforderungen adressiert. Die Beiträge dieser Arbeit lassen sich in drei Kernbereiche aufteilen: Planung und Umplanung von Feature Modell Evolution, Analyse von Feature Modell Evolution und konsistente Evolution von SPL Artefakten. Temporal Feature Models (TFMs) werden als Startpunkt für SPL Evolution eingeführt. In einem TFM wird die gesamte Evolutionszeitlinie eines Feature Modells in einem Artefakt abgebildet, was sowohl vergangene Änderungen, den aktuellen Zustand, als auch geplante Änderungen beinhaltet. Auf Basis einer Ausführungssemantik wird die Konsistenz von Feature Modell Evolutionszeitlinien sichergestellt. Um Feature Modelle frei von Anomalien zu halten, werden Analysen eingeführt, welche die gesamte Evolutionszeitlinie eines Feature Modells auf Anomalien untersucht und diese mit verursachenden Evolutionsoperationen erklärt. Das Konzept zur Modellierung von Feature Modell Evolutionszeitlinien aus TFMs wird verallgemeinert, um die gesamte Evolution von Modellen beliebiger Modellierungssprachen spezifizieren zu können. Des Weiteren wird eine Methodik vorgestellt, die beteiligten Ingenieuren eine geführte Evolution von Konfigurationen ermöglicht

Efficient Automated Planning with New Formulations

Problem solving usually strongly relies on how the problem is formulated. This fact also applies to automated planning, a key field in artificial intelligence research. Classical planning used to be dominated by STRIPS formulation, a simple model based on propositional logic. In the recently introduced SAS+ formulation, the multi-valued variables naturally depict certain invariants that are missed in STRIPS, make SAS+ have many favorable features. Because of its rich structural information SAS+ begins to attract lots of research interest. Existing works, however, are mostly limited to one single thing: to improve heuristic functions. This is in sharp contrast with the abundance of planning models and techniques in the field. On the other hand, although heuristic is a key part for search, its effectiveness is limited. Recent investigations have shown that even if we have almost perfect heuristics, the number of states to visit is still exponential. Therefore, there is a barrier between the nice features of SAS+ and its applications in planning algorithms. In this dissertation, we have recasted two major planning paradigms: state space search and planning as Satisfiability: SAT), with three major contributions. First, we have utilized SAS+ for a new hierarchical state space search model by taking advantage of the decomposable structure within SAS+. This algorithm can greatly reduce the time complexity for planning. Second, planning as Satisfiability is a major planning approach, but it is traditionally based on STRIPS. We have developed a new SAS+ based SAT encoding scheme: SASE) for planning. The state space modeled by SASE shows a decomposable structure with certain components independent to others, showing promising structure that STRIPS based encoding does not have. Third, the expressiveness of planning is important for real world scenarios, thus we have also extended the planning as SAT to temporally expressive planning and planning with action costs, two advanced features beyond classical planning. The resulting planner is competitive to state-of-the-art planners, in terms of both quality and performance. Overall, our work strongly suggests a shifting trend of planning from STRIPS to SAS+, and shows the power of formulating planning problems as Satisfiability. Given the important roles of both classical planning and temporal planning, our work will inspire new developments in other advanced planning problem domains

Bounded Situation Calculus Action Theories

In this paper, we investigate bounded action theories in the situation calculus. A bounded action theory is one which entails that, in every situation, the number of object tuples in the extension of fluents is bounded by a given constant, although such extensions are in general different across the infinitely many situations. We argue that such theories are common in applications, either because facts do not persist indefinitely or because the agent eventually forgets some facts, as new ones are learnt. We discuss various classes of bounded action theories. Then we show that verification of a powerful first-order variant of the mu-calculus is decidable for such theories. Notably, this variant supports a controlled form of quantification across situations. We also show that through verification, we can actually check whether an arbitrary action theory maintains boundedness.Comment: 51 page

SDRL: Interpretable and Data-efficient Deep Reinforcement Learning Leveraging Symbolic Planning

Deep reinforcement learning (DRL) has gained great success by learning directly from high-dimensional sensory inputs, yet is notorious for the lack of interpretability. Interpretability of the subtasks is critical in hierarchical decision-making as it increases the transparency of black-box-style DRL approach and helps the RL practitioners to understand the high-level behavior of the system better. In this paper, we introduce symbolic planning into DRL and propose a framework of Symbolic Deep Reinforcement Learning (SDRL) that can handle both high-dimensional sensory inputs and symbolic planning. The task-level interpretability is enabled by relating symbolic actions to options.This framework features a planner -- controller -- meta-controller architecture, which takes charge of subtask scheduling, data-driven subtask learning, and subtask evaluation, respectively. The three components cross-fertilize each other and eventually converge to an optimal symbolic plan along with the learned subtasks, bringing together the advantages of long-term planning capability with symbolic knowledge and end-to-end reinforcement learning directly from a high-dimensional sensory input. Experimental results validate the interpretability of subtasks, along with improved data efficiency compared with state-of-the-art approaches