A Least-Commitment Approach to Planning Construction Projects with Repeated Cycles of Operation

Knowledge representation and reasoning techniques derived from Artificial Intelligence (AI) research permit computers to generate plans, not merely analyze plans generated by human planners. They explicitly represent knowledge about how generate plans in the form of initial and goal states, descriptions of actions along with their preconditions and effects, and a control structure for selecting new actions to insert into a project plan. From the more than two dozen AI planners developed and published since the 1960’s, we have chosen SIPE (System for Interactive Planning and Execution monitoring) to investigate the utility of AI planners for construction project planning. This paper presents our experience implementing SIPE to plan a multi-story office building construction project with repeated cycles of operation. A least-commitment approach, which aims to delay decisions concerning ordering links and variable instantiations until the system has as much useful information as possible for making them, has proven to be very useful for planning this kind of construction projects. With the use of a frame hierarchy, generic operators, and a least-commitment approach, SIPE is able to generate logically correct activity networks for multi-story building construction from a description of components of a facility. To model such construction projects in a concise and uniform framework, we show the usefulness of some underlying principles for establishing ordering relationships among the project components involved in construction activities.