Constraint-based Automated Planning and Business Process Modelling

Nina Ghanbari Ghooshchi, Abdul Sattar, University, My
Business processes are essential parts of any organisation which define the series of steps performed to achieve their goal. Business processes are normally designed manually by business experts who have deep knowledge of the activities performed in the organi-sations. This knowledge is commonly described in a declarative way as business rules. Organisations have to cope with large numbers of business rules and existing regulations governing the business in which they operate. Such rules are
more » ... . Such rules are difficult to maintain due to their size and complexity, and it is increasingly challenging to ensure that each business process adheres to those rules. As such, extraction of business processes from rules has three clear advantages: (1) visualisation of all possible executions allowed by the rules,(2) automated execution and compliance by design, (3) identification of "inefficiencies" in the business rules. Extraction of business processes from rules set is a time and re-source consuming process. In this thesis, we have investigated two approaches to extract the business processes from the declarative specification of it. In the first approach, we have investigated the application of constraint satisfaction based planners to automatically extract the business process from the sets of rules specifying it. For this purpose, we have developed a constraint-based automated planner named Transition Constraints for Parallel Planning (TCPP). As a constraint-based planner, it encodes a planning problem as a constraint satisfaction problem and then extracts the plan from the solution to that. TCPP constructs its constraint model from a redefined version of the domain transition graphs (DTG) of a given planning problem. TCPP encodes state transitions in the redefined DTGs by using table constraints with cells containing don't cares or wild cards. TCPP uses Minion the constraint solver to solve the constraint model and returns a parallel plan. We empirically compare TCPP with the other state-of-the-art constraint-based parallel planners. Experimen [...]
doi:10.25904/1912/3980 fatcat:dx4ob22amnd5vlczvty6ncjh24