Parallel behavior composition for manufacturing

A key problem in the manufacture of highlycustomized products is the synthesis of controllers able to manufacture any instance of a given product type on a given production or assembly line. In this paper, we extend classical AI behavior composition to manufacturing settings. We first introduce a novel solution concept for manufacturing composition, target production processes, that are able to manufacture multiple instances of a product simultaneously in a given production plant. We then propose a technique for synthesizing the largest target production process, together with an associated controller for the machines in the plant

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