Behavior composition optimisation

The behavior composition problem involves automatic synthesis of a controller that is able to “realize” (i.e., implement) a desired target specification by suitably controlling a collection of already available, partially controllable, behaviors running in a partially predictable shared environment. A behavior in our context refers to an already existing functionality such as the logic of a device, a service, a standalone component, etc; whereas a target specification represents the desired non-existent functionality that is meant to be obtained through the available behaviors. Previous work in behavior composition has exclusively aimed at synthesising exact controllers, those that bring about the desired specification completely. One open issue has resisted principled solutions: if the target specification cannot be completely implemented, is there a way to realize it “optimally”? In this doctoral thesis, we propose qualitative and quantitative optimisation frameworks that are able to accommodate composition problems that do not admit the “perfect” coordinating controller. In the qualitative setting, we rely on the formal notion of simulation to define realizable fragments of a target specification and show the existence of a unique supremal realizable fragment for a given problem instance. In addition, we extend the qualitative framework by introducing exogenous uncontrollable events to represent observable contingencies. In the quantitative setting, we provide a decision theoretic approach to behavior composition by quantifying the uncertainties present in the domain. In all cases, we provide effective techniques to compute optimal solutions and study their computational properties

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