Mining CSTNUDs significant for a set of traces is polynomial

A Conditional Simple Temporal Network with Uncertainty and Decisions (CSTNUD) is a formalism for temporal plans that models controllable and uncontrollable durations as well as controllable and uncontrollable choices simultaneously. In the classic top-down model-based engineering approach, a designer builds CSTNUDs to model, validate and execute some temporal plans of interest. In this paper, we investigate a bottom-up approach by providing a deterministic polynomial time algorithm to mine a CSTNUD from a set of execution traces (i.e., a log). We provide a prototype implementation and we test it with a set of artificial data. Finally, we elaborate on consistency and controllability of mined networks

Mining temporal networks: Results and open problems

none3noThe design of temporal networks typically follows a top-down approach where a designer handcrafts a temporal network to model some concrete plan of interest. Instead, the bottom-up approach of mining is the process of building a temporal network from a set of execution traces of some (typically unknown) underlying process. Recent research showed that, due to the structural properties of temporal networks, such a task can be done in polynomial time. In this paper, we give an overview of the current status of our research and highlight open problems concerning Formal Methods and Artificial Intelligence.noneSciavicco G.; Villa T.; Zavatteri M.Sciavicco, G.; Villa, T.; Zavatteri, M

On the Complexity of Resource Controllability in Business Process Management

Resource controllability of business processes (BPs) is the problem of executing a BP by assigning resources to tasks, while satisfying a set of constraints, according to the outcome of a few uncontrollable events that we only observe during execution. Recent research addressed resource controllability of acyclic BPs where the choices of the XOR paths to take were out of control. However, a formal model of BP to reason on resource controllability is still missing. Thus, the precise mathematical definitions of controllability problems, their semantics and complexity analysis, have remained unexplored. To bridge this gap, we propose a hierarchy of 8 classes of Business Processes with Resources and Uncertainty (BPRUs) to address controllable and uncontrollable resource assignments in combination with controllable and uncontrollable choices of the XOR paths to take. We define consistency of BPRs (i.e., BPRUs without uncertainty) and prove that deciding it is NP-complete. We define strong controllability of BPRUs and prove that deciding it is either NP-complete or Sigma_2^p-complete depending on the class. We define weak and dynamic controllability of BPRUs and prove that deciding them is Pi_2^p-complete and PSPACE-complete, respectively

Conditional Uncertainty in Constraint Networks

Constraint Networks (CNs) are a framework to model the Constraint Satisfaction Problem (CSP), which is the problem of finding an assignment of values to a set of variables satisfying a set of given constraints. Therefore, CSP is a satisfiability problem. When the CSP turns conditional, consistency analysis extends to finding also an assignment to these conditions such that the relevant part of the initial CN is consistent. However, CNs fail to model CSPs expressing an uncontrollable conditional part (i.e., a conditional part that cannot be decided but merely observed as it occurs). To bridge this gap, in this paper we propose Constraint Networks Under Conditional Uncertainty (CNCUs), and we define weak, strong and dynamic controllability of a CNCU. We provide algorithms to check each of these types of controllability and discuss how to synthesize (dynamic) execution strategies that drive the execution of a CNCU saying which value to assign to which variable depending on how the uncontrollable part behaves. We discuss Zeta, a tool that we developed for CNCUs to carry out an experimental evaluation. What we propose is fully automated from analysis to simulation