1. School of Computing and Mathematics, University of Huddersfield Week 21: Knowledge Acquisition / GIPO Lee McCluskey, room 2/09 Email lee@hud.ac.uklee@hud.ac.uk http://scom.hud.ac.uk/scomtlm/cha2555/

2. School of Computing and Mathematics, University of Huddersfield Knowledge Acquisition n Knowledge Acquisition is the process of eliciting and encoding knowledge in a way that intelligent processes can use effectively. n AI systems dealing with SPECIFIC applications need a Knowledge Acquisition phase ? Do we always need an AI expert to encode knowledge? ? Can we get programs to learn – or acquire knowledge for themselves ?

3. School of Computing and Mathematics, University of Huddersfield Knowledge Acquisition- example KB [(one_or_both_of Segment1 and Segment2 are_flown_at_subsonic_speed) & ( the_Aircraft_on(Segment1) and the_Aircraft_on(Segment2) meet_mnps) & ( the_Aircraft_on(Segment1) and the_Aircraft_on(Segment2) are_jets & (the_Profile_containing(Segment1) & the_Profile_containing(Segment2) are_wholly_or_partly_in_the_ mnps_airspace) ] => [(the_basic_min_longitudinal_sep _Val_in_mins_required_for Segment1 and Segment2) = 10 …. ETC

4. School of Computing and Mathematics, University of Huddersfield Knowledge Acquisition n KA originates from the old idea of 'knowledge transfer', where constructing a KBS amounted to extracting the knowledge from experts and encoding it within an expert system 'shell‘ Application expertise transfer Procedural expert knowledge In the 1980s AI encountered the “KA bottleneck” - it proved too costly/hard to encode knowledge for every new KBS application

5. School of Computing and Mathematics, University of Huddersfield Knowledge Acquisition for AI Planning: Definition Knowledge Acquisition in AI Planning is the process that deals with the capture, formulation, validation and maintenance of planning domain models A planning domain model contains… u 1. Domain Structure (objects, constraints) u 2. Domain Operators (dynamic knowledge) u 3. Domain-specific Heuristics GIPO helps capture 1+2

6. School of Computing and Mathematics, University of Huddersfield GIPO - rationale Planning Domain Models are hard to design, write, debug, maintain - even for experts. The process of encoding is laborious. Bugs are of various types can lurk in models for a long time. As planners and planning applications become larger, the problems of engineering planning domain models become more acute. There is a need to build engineering environments and explore their synergy with general purpose planners. Acquisition is Very hard!! Application Domain Model

7. School of Computing and Mathematics, University of Huddersfield GIPO – what is it? GIPO (Graphical Interface for Planning with Objects) is an experimental GUI and tools environment for building planning domain models. u It is written mainly in Java, with some embedded tools in Prolog, and is under continuous development. u It won the “best general tools” award in the First International Competition on Knowledge Engineering for Planning and Scheduling, at Monterey, California, 2005 u It is a product of PLANFORM, a UK EPSRC-funded research project, written at The University of Huddersfield UK. Website: http://scom.hud.ac.uk/planform http://scom.hud.ac.uk/planform

8. School of Computing and Mathematics, University of Huddersfield GIPO http://scom.hud.ac.uk/planform/gipo/

9. School of Computing and Mathematics, University of Huddersfield GIPO – main tools + errors removed BASIC INTERFACE: n graphical tools for input/display of sorts - syntax and semantic checks for individual components of a model + graphical tools for building operators Prevents misspelling of predicate names, prevents mis-use of the same name to denote different objects or classes. n validation checks: checks for clashes between different parts of a model eg sort hierarchy consistency GIPO III INTERFACE: n most of the above are sidetracked as GIPO generates the model

10. School of Computing and Mathematics, University of Huddersfield GIPO – main tools + errors removed n stepper - allow a user to build up their own solutions to problems inappropriate preconditions in an operator schema can be detected when building up a solution. The stepper both detects and helps remove such errors. n animator – visualise a planner's solution to a problem in terms of object transitions Missing parts of a model eg missing operator schema can be detected by the stepper/animator n graphical window for composing operators This prevents errors actually being introduced as it allows only valid components to make up operator schema n Other tools: translation to PDDL, API for plugging in to third party planner, a random task generator -- all help in the VALIDATION of a model.

11. School of Computing and Mathematics, University of Huddersfield GIPO III interface n 1) Draw machines for individual sorts u Rectangles – depict the space of states of each sort eg person can be fit, tired or inCar. u Green ovals – named transitions that change the state of objects u Blue ovals – named transitions that change the value of properties of objects (as well as possibly the object’s state) n 2) Next: Put in: u Prevail conditions – single red arrows u Necessary/conditional transitions – double headed arrows u Object constraints: whether object associations persist or desist ‘+’ indicates that a transition must remember an object association

12. School of Computing and Mathematics, University of Huddersfield Example: The Lazy Hiking World Imagine Sue and Fred want to have a hiking holiday in the Lake District in North West England. They walk in one direction, and do one ``leg'' each day. But not being very fit, they use two cars to carry them / the tent / their luggage to the start/end of a leg. They must have their tent up already so they can sleep the night, before they set off again to do the next leg in the morning. Actions include walking, driving, moving and erecting tents, and sleeping. The requirement for the planner is to work out the logistics and generate plans for each day of the holiday. Helvelyn Fairfield Coniston

13. School of Computing and Mathematics, University of Huddersfield GIPO III interface

14. School of Computing and Mathematics, University of Huddersfield Transition Co-ordination Transitions Requires Object at State Transition Dependent on Source Both satisfy next(x,y) Transitions mutually dependent Both satisfy next(x,y) Add Association Record car Break Association Forget Car Capturing the Lazy Hiking World using GIPO III’s Life History Editor

15. School of Computing and Mathematics, University of Huddersfield Conclusion n AI Planning technology needs to made more usable and available n The main assumption in AI Planning is that there should be a logical separation between the planning engine and the domain model. Particularly important is the ACQUISITION of the domain model – if this has bugs then the application is doomed n GIPO is an experimental GUI for building and validating planning domain models. It is a first step in making planning technology more usable.