1. Feasibility Criteria for Investigating Potential Application Areas of AI Planning T.L.McCluskey, The University of Huddersfield,UK Email lee@hud.ac.uklee@hud.ac.uk

2. Knowledge Engineering and Intelligent Interfaces Automated Planning Technology..reasons with EXPLICIT representations of actions, constraints, goals.. in order to synthesise plans and schedules Sounds like it would be widely applicable software? A B B A

3. Knowledge Engineering and Intelligent Interfaces Summary of Talk Specialised Commercial Applications of Planning Generalised Application within an “Agent” or as a “Service” ? ?

4. Knowledge Engineering and Intelligent Interfaces Related Work Evaluation Criteria similar in parts to ANY introduction of (advanced) technology, especially introduction of IKBS However – our work is much more specific within application area, and technology being introduced Past applications of AI Planning tend to skirt around issues of feasibility – they are a successful application already!

5. Knowledge Engineering and Intelligent Interfaces Applications considered 1. Air Traffic Control (old one) 2. Water / Flood Management 3. Road Traffic Management

6. Knowledge Engineering and Intelligent Interfaces Air Traffic Management

7. Knowledge Engineering and Intelligent Interfaces Air Traffic Management Problem: Hundreds of Flights over North Atlantic every day, these have to be planned the day before, then re-planned ½ hour before plane entry if a conflict probe shows a potential problem. two potential applications of AI planning: - Advanced (day before) flight planning -En Route planning Actions include feasible aircraft movement Goals are (a) safety – not violating conflict zones (b) fuel efficiency and passenger comfort

8. Knowledge Engineering and Intelligent Interfaces Water / Flood Management GIS tool from ESRI

9. Knowledge Engineering and Intelligent Interfaces Water / Flood Management involves local and national authorities, service industries, and research institutes. two potential applications of AI planning: n for long term planning of infrastructure to prevent or lessen the risk of flooding - climatic change and population change, and may involve flood defence design or even river design n for real-time planning to support flood event management. - under the heading of crisis management, and may incorporate evacuation management. Actions in the latter include movement of evacuation assets, deployment of emergency service, and information acquisition and information dissemination Goals would involve minimising the loss of life and property

10. Knowledge Engineering and Intelligent Interfaces Road Network Management n Complex data gathering, knowledge extraction, planning and control application n Critical to congestion control, incident management, road use optimisation

11. Knowledge Engineering and Intelligent Interfaces Road Traffic Management many systems for data gathering (various types of traffic detectors, cctv) Many organisations and regulatory bodies involved (complex set of stakeholders) potential application of AI planning: for day to day, event and crisis planning Actions include traffic lights, variable speed limits, variable message signs, local radio, satnav Goal: to form and execute a plan to control and hence optimise the network

12. Knowledge Engineering and Intelligent Interfaces A proposed system architecture

13. Knowledge Engineering and Intelligent Interfaces Is the Application of AI Planning Feasible? Evaluation Criteria 1.Motivation Factors 1. cost savings? 2. quality of service? 2.Technological Context and Human Factors 1. Existing technological infrastructure 2. Data availability, form, quality (both real-time AND historical) 3. Acceptance of Innovation by Users 3.Knowledge Engineering Factors 1. Closeness to previous applications 2. Procedure formalisation in the problem area 3. Appropriateness for AI planning solution

14. Knowledge Engineering and Intelligent Interfaces Evaluation: Air Traffic Management 1.Motivation Factors [FAIL] n Unclear that the innovation would improve current system n ‘intelligent’ systems not embraced quickly because of safety critical nature of tasks 2.Technological Context and Human Factors [PASS] n Data availability excellent n Few current systems, all reasonably interoperable, but systems for different areas (Oceanic, Domestic) n Record of technological innovation patchy 3.Knowledge Engineering Factors [PASS] n Excellent as we had already produced a formal model of the environment and separation criteria in previous work! n Planning problem fairly straightforward if a little numerical

15. Knowledge Engineering and Intelligent Interfaces Evaluation: Flood Event Management 1.Motivation Factors [PASS] n the production of sound plans in real time which are higher quality than human produced (human track record not good) n pre-event generation of emergency plans is infeasible (FLOODsite EU project) 2.Technological Context and Human Factors [PASS] n use of DSS systems n quality of data (eg satnav) good, and flood simulation/prediction good n Human acceptance of technology good, but users not tech experts 3.Knowledge Engineering Factors [BORDERLINE] n past work: evacuation/crisis management fairly well researched n complex planning problem involving continuous processes and uncertainty

16. Knowledge Engineering and Intelligent Interfaces Evaluation: Road Traffic Management 1.Motivation Factors [pass] n with complex data, goals, actions, RTM is becoming too complicated for human control? n road management experts scarce 2.Technological Context and Human Factors [pass] n AI systems already deployed in Transport eg Scoot n culture of technological innovation n excellence amounts of historical data and experimental platforms n But many different systems, not all interoperable 3.Knowledge Engineering Factors [borderline]  road strategies / plans semi-formalised  some similar benchmark planning domains but not on the huge scale of a road network

17. Knowledge Engineering and Intelligent Interfaces Summary It is very difficult to find specialised application areas for automated planning – either conditions have to be just right OR a lot of extra effort is needed and the application is not (in the short run) cost effective... AI Planning would be better off embedded within services