Felix Mohr

Automated Online Service Composition

Services are self-contained and platform independent software components that aim at maximizing software reuse. The automated composition of services to a target software artifact has been tackled with many AI techniques, but existing approaches make unreasonably strong assumptions such as a predefined data flow, are limited to tiny problem sizes, ignore non-functional properties, or assume offline service repositories. This paper presents an algorithm that automatically composes services without making such assumptions. We employ a backward search algorithm that starts from an empty composition and prep ends service calls to already discovered candidates until a solution is found. Available services are determined during the search process. We implemented our algorithm, performed an experimental evaluation, and compared it to other approaches.

An approach towards adaptive service composition in markets of composed services

On-the-fly composition of service-based software solutions is still a challenging task. Even more challenges emerge when facing automatic service composition in markets of composed services for end users. In this paper, we focus on the functional discrepancy between “what a user wants” specified in terms of a request and “what a user gets” when executing a composed service. To meet the challenge of functional discrepancy, we propose the combination of existing symbolic composition approaches with machine learning techniques. We developed a learning recommendation system that expands the capabilities of existing composition algorithms to facilitate adaptivity and consequently reduces functional discrepancy. As a representative of symbolic techniques, an Artificial Intelligence planning based approach produces solutions that are correct with respect to formal specifications. Our learning recommendation system supports the symbolic approach in decision-making. Reinforcement Learning techniques enable the recommendation system to adjust its recommendation strategy over time based on user ratings. We implemented the proposed functionality in terms of a prototypical composition framework. Preliminary results from experiments conducted in the image processing domain illustrate the benefit of combining both complementary techniques.

Combining Automatic Service Composition with Adaptive Service Recommendation for Dynamic Markets of Services

Automatic service composition is still a challenging task. It is even more challenging when dealing with a dynamic market of services for end users. New services may enter the market while other services are completely removed. Furthermore, end users are typically no experts in the domain in which they formulate a request. As a consequence, ambiguous user requests will inevitably emerge and have to be taken into account. To meet these challenges, we propose a new approach that combines automatic service composition with adaptive service recommendation. A best first backward search algorithm produces solutions that are functional correct with respect to user requests. An adaptive recommendation system supports the search algorithm in decision-making. Reinforcement Learning techniques enable the system to adjust its recommendation strategy over time based on user ratings. The integrated approach is described on a conceptional level and demonstrated by means of an illustrative example from the image processing domain.

Semi-Automated Software Composition Through Generated Components

Software composition has been studied as a subject of state based planning for decades. Existing composition approaches that are efficient enough to be used in practice are limited to sequential arrangements of software components. This restriction dramatically reduces the number of composition problems that can be solved. However, there are many composition problems that could be solved by existing approaches if they had a possibility to combine components in very simple non-sequential ways. To this end, we present an approach that arranges not only basic components but also composite components. Composite components enhance the structure of the composition by conditional control flows. Through algorithms that are written by experts, composite components are automatically generated before the composition process starts. Therefore, our approach is not a substitute for existing composition algorithms but complements them with a preprocessing step. We verified the validity of our approach through implementation of the presented algorithms.

Template-Based Generation of Semantic Services

There are many technologies for the automation of processes that deal with services; examples are service discovery and composition. Automation of these processes requires that the services are described semantically. However, semantically described services are currently not or only rarely available, which limits the applicability of discovery and composition approaches. The systematic support for creating new semantic services usable by automated technologies is an open problem.

A Metric for Functional Reusability of Services

Services are self-contained software components that can be used platform independent and that aim at maximizing software reuse. A basic concern in service oriented architectures is to measure the reusability of services. One of the most important qualities is the functional reusability, which indicates how relevant the task is that a service solves. Current metrics for functional reusability of software, however, have very little explanatory power and do not accomplish this goal.

Estimating Functional Reusability of Services

Services are self-contained software components that can be used platform independent and that aim at maximizing software reuse. A basic concern in service oriented architectures is to measure the reusability of services. One of the most important qualities is the functional reusability, which indicates how relevant the task is that a service solves. Current metrics for functional reusability of software, however, either require source code analysis or have very little explanatory power. This paper gives a formally described vision statement for the estimation of functional reusability of services and sketches an exemplary reusability metric that is based on the service descriptions.

Reducing Nondeterminism in Automated Service Composition

Automated service composition has been studied as a subject of state based planning for a decade. A great deal of service composition tasks can only be solved if concrete output values of the services are considered in the composition process. However, the fact that those values are not known before runtime leads to nondeterministic planning problems, which have proven to be notoriously difficult in practical automated service composition applications. Even though this problem is frequently recognized, it has still received remarkably few attention and remains unsolved. This paper shows how nondeterminism in automated service composition can be reduced. We introduce context rules as a means to derive semantic knowledge from output values of services. These rules enable us to replace nondeterministic composition operations by less nondeterministic or even completely deterministic ones. We show the validity of our solutions not only theoretically but also have evaluated them practically through implementation.

ML-Plan: Automated machine learning via hierarchical planning

Automated machine learning (AutoML) seeks to automatically select, compose, and parametrize machine learning algorithms, so as to achieve optimal performance on a given task (dataset). Although current approaches to AutoML have already produced impressive results, the field is still far from mature, and new techniques are still being developed. In this paper, we present ML-Plan, a new approach to AutoML based on hierarchical planning. To highlight the potential of this approach, we compare ML-Plan to the state-of-the-art frameworks Auto-WEKA, auto-sklearn, and TPOT. In an extensive series of experiments, we show that ML-Plan is highly competitive and often outperforms existing approaches.

Applying Reinforcement Learning for Resolving Ambiguity in Service Composition

Automatically composing service-based software solutions is still a challenging task. Functional as well as non-functional properties have to be considered in order to satisfy individual user requests. Regarding non-functional properties, the composition process can be modeled as optimization problem and solved accordingly. Functional properties, in turn, can be described by means of a formal specification language. State-space based planning approaches can then be applied to solve the underlying composition problem. However, depending on the expressiveness of the applied formalism and the completeness of the functional descriptions, formally equivalent services may still differ with respect to their implemented functionality. As a consequence, the most appropriate solution for a desired functionality can hardly be determined without considering additional information. In this paper, we demonstrate how to overcome this lack of information by means of Reinforcement Learning. In order to resolve ambiguity, we expand state-space based service composition by a recommendation mechanism that supports decision-making beyond formal specifications. The recommendation mechanism adjusts its recommendation strategy based on feedback from previous composition runs. Image processing serves as case study. Experimental results show the benefit of our proposed solution.