Using GIPO to support learning in knowledge acquisition and automated planning

1

• Using GIPO to support learning in knowledge
  acquisition and automated planning

Lee McCluskey and Ron Simpson
2
Contents

• Part A Some Problems with Teaching AI
• Part B GIPO
• Part C Student Learning Experience with GIPO
• Part D Comparison to other similar tools used
  in UG computing teaching
• Part E Conclusions

3
PART ASome Problems with Teaching AI
4
Problems with AI Teachingin practical work

• We all know that areas such as ..
• knowledge representation
• automated reasoning
• are difficult for students to grasp
• .. to make it easier practical examples in
  symbolic AI often start with cleaned, crafted
  knowledge structures abstracting out the
  knowledge acquisition and formulation process
• Eg in ATP we start with exactly the axioms we
  need to do a proof.
• Eg in AI planning we start with exactly the right
  action structures we need to form a plan.

5
Problems with AI Teaching in practical work

• (define (domain rocket-domain)
• (requirements typing equality)
• (types rocket place cargo)
• (predicates (at ?x - (either rocket cargo) ?p -
  place) (has-fuel ?r - rocket) (in ?c - cargo ?r -
  rocket))
• (action move
• parameters (?r - rocket ?from ?to - place)
• precondition (and (at ?r ?from) (has-fuel ?r)
  (not ( ?from ?to)))
• effect (and (not (at ?r ?from)) (at ?r ?to)
  (not (has-fuel ?r)))) ETC .

6
Problems with AI Teaching - use of declarative
programming languages in AI practicals

• Declarative PLs have their role in teaching AI
  practicals
• we can use them to rapidly implement AI search
  methods and algorithms
• But.. they are still low level in the context
  of the range of AI topics its not easy to lead
  students to build or integrate advanced AI
  functions from the basis of a programming
  language.
• The tutor would implement AI algorithms to expose
  their workings, but knowledge intensive issues
  such as domain modelling are be harder to
  illustrate using a declarative language on its
  own.

7
Summary

• Acquiring, debugging and crafting knowledge bases
  is another factor as to why the teaching of AI is
  difficult
• The process of how knowledge is acquired is not
  easy for a student to grasp without practical
  experience of the process just explaining the
  theory can be boring for the student. This is
  difficult to explain using a declarative PL

8
PART B using GIPO the 'Graphical Interface for
Planning with Objects
9
One possible solution.. Use of an integrated
tools environment

• Such a tool should
• Have a simple, familiar look and feel - make it
  easier to learn
• connect together a range of theory taught during
  lectures with the application of the theory
  during practical classes - students havent time
  to learn many tools
• relate AI to other subject areas taught at
  undergraduate level computing - students need
  the curriculum to fit together

10
GIPO

• 'Graphical Interface for Planning with Objects
• http//scom.hud.ac.uk/planform/gipo
• is the name of a family of experimental tools
  environment for building planning domain models,
  and doing automated planning
• GIPO won the first knowledge engineering for
  planning competition (ICKEPS05) in the general
  tools class at ICAPS 2005, Monterey, California

11
/
12
Knowledge Representation

• The object metaphor guides the domain designer in
  structuring the domain definition - plan
  execution involves changing the state of a subset
  of objects within the sphere of interest from an
  initial state to a desired goal state
• Potential student learning is the use of
  logic/object representations, in the area of
  dynamic systems - representing time and change,
  representing actions, events, processes

13
Knowledge Acquisition / Formulation

• Multiple methods
• Follow manual Object Centric Method.
• Define Object Types - Predicates - Object State
  Invariants Operators - Methods
• Operator Induction (GIPO II) OpMaker
• Define Object Types - Predicates - Object States
  
• Input plan examples
• Output operator schema
• Draw Object Life Histories (GIPO III)
• Draw stylised state transition diagrams defining
  the possible state transitions for each type of
  object and define the connections between the
  transition diagrams.
• Use re-usable fragments of domains stored in
  library

14
Domain Analysis

• Static Analysis
• Syntax / structural consistencies checked
• Only legal domain specification will be produced
  when checks passed. The formulation of state
  invariants exclude potential errors.
• Semantics
• Eg State usage analysis reveals states that form
  dead-ends and states that cannot be generated.
• Eg Transparency analysis in hierarchical models
  guarantees that methods do achieve their
  post-conditions when their pre-conditions are
  met.
• Dynamic Testing
• Animators to help inspect generated plans
  produced by integrated planning engines.
• Manual steppers to allow developer to check that
  domain definition does support known plans.

15
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.
Fairfield
Helvelyn
Coniston
16
Object Centric View

• Plans are strategies to bring about changes in
  the states of objects within the domain problem.
• Objects have state and properties.
• Actions bring changes of state and/or property
  values.
• Properties are present in all states

Tent
Transition Descriptors
State Descriptors
17
Hiking Domain Example
Transition Property Value Changing Satisfies
next(x,y) nextStage(w,v) Constraint
Number Satisfies couple(x,y)
Transition State Changing
Tent Property Location Value present in
all identified states.
Tent
Person
Car
Person Properties Location Stage
Transition of Tent Property Value
Changing Location to Location
18
Transition Co-ordination
Transitions Requires Object at State
Break Association Forget Car
Add Association Record car
Transition Dependent on Source Both satisfy
next(x,y)
Transitions mutually dependent Both satisfy
next(x,y)
19
Libraries of Generic Types

• Object Life Histories share common structure that
  are re-usable.
• Define Public interface i.e states and
  transitions that support merges.
• Complexity of structures such as block stacks can
  be hidden in packages.
• Users can save fragments of their own domains to
  the library

20
Richer Representations

• Durative Actions
• PDDL Level 5 equivalent
• Processes.
• Events.
• Numeric Properties.
• Supports
• Domain Design using Life Histories.
• Manual Plan stepping
• No integrated planner at present time.

21
Dynamic Testing

• Static Analysis may Indicate problems otherwise
  Manual Stepping may reveal source of problem.

22
PART C Student Learning Experience with GIPO
23
Student Learning - Caveat

• GIPO
• was not designed for teaching, but as an
  experimental platform for KE for planning
• assumes that the user has to shoe horn the
  planning problem into its own object centric
  world view

24
Student Learning

• GIPO
• provides learning support for
• knowledge acquisition and formulation, validation
  and maintenance of (planning) domain models,
• inductive learning
• automated plan generation and plan execution.
• has online documentation
• tutorial introductions,
• a user manual,
• a more in-depth language manual which defines the
  underlying knowledge representation language.
• has been used with intermediate and final year
  undergraduates (typically 15-20). Anecdotal
  evidence indicates that GIPO helps students
  integrate AI knowledge learned in lectures, and
  to reach a deeper level of understanding of
  'dry' subject matter on say the acquisition of
  knowledge.

25
Student Learning - method

• We have found it useful to present the student
  with two paths through the material
• an online tutorial on how to construct domains
  the student is led through a staged method of
  domain development
• analysis and execution of a 'ready-cooked' domain
  model this way the student can at an early stage
  see the result of domain building - being able to
  bind the model with a planner of choice and being
  able to solve planning problems.

26
Student Learning some examples

• Using the online tutorial the students learns the
  difficulty in formulating knowledge about actions
• Using OpMaker the student learns the
  potential/problems of machine learning techniques
  
• - one can avoid the process of hand crafting
  action knowledge
• - the problems and limitations of learning from
  examples to do with convergence of
  generalisations, the need for knowledge
  refinement and the importance of 'good' examples
  in learning.
• Using GIPOs DYNAMIC and STATIC testing tools
  the student learns about validation how to
  uncover and fix two main types of errors
• Type 1. checking the model for inconsistencies
  between component parts
• Type 2. checking the model's accuracy with
  respect to what is being modelled.

27
Student Learning connections to other computing
areas

• Object metaphor and staged acquisition process
  akin to the use of UML/OOAD
• Pre- and post-condition, deterministic,
  instantaneous actions in terms of predicate
  descriptions version of a GIPO operator similar
  to formal specifications of actions eg in B.
• Verification and validation in general software
  design

28
PART D Comparison to other similar tools used
in UG computing teaching
29
Comparison The B-Tool

• The B-toolkit has been successfully used in
  teaching formal methods in our own curriculum for
  several years to MSc, BSc and HND students.
• Supporting the method are tools that alleviate
  some of the problems in the onerous task of
  constructing and discharging proof obligations.
• The student can understand the need for rigor and
  view the effects of proof tools, without the need
  for them to produce hand proofs themselves.

30
Comparison The B-Tool

• Similarities with GIPO/AI Planning
• the concept of a 'state'
• the need to construct operators in terms of pre-
  and post-conditions and state transitions
• the underlying assumptions of default persistence
  and the 'closed world'
• the use of invariants to help in validation and
  model documentation.

31
Comparison The B-Tool

• Differences with AI Planning
• the objectives of both tools are different - one
  to rigorously develop software, the other to
  develop an application that solves planning
  problems.
• In general the B-tool allows the user to input
  more precise details of a domain, and is more
  meticulous at uncovering errors.
• On the other hand, GIPO's range of dynamic tools
  (the stepper and the use of plan generators) give
  it an extra dimension that both stimulates
  students and allows more scrutiny of the domain
  model components.
• One can simulate operator execution using the
  B-tool (and hence this gives a primitive plan
  stepper), but not plan generation as with GIPO.

32
Comparison Protégé

• is a well established knowledge acquisition tool
  which aids the user in building up domain models
  in description logic. As with the B-tool/GIPO, it
  was not designed originally for use in teaching
  and learning
• Our final year undergraduate students are exposed
  to Protégé-OWL in a module entitled "The Semantic
  Web".
• Protégés interface is similar in some ways to
  GIPO - the usual array of GUI features can be
  used to build up object hierarchies and input
  propositions and class constraints.

33
Comparison Protégé

• Has very good online tutorials that slowly build
  up a student's knowledge of relevant features
• A DL theorem prover such as RACER can be hooked
  up to check classes for consistency
• Class hierarchies can be re-assembled as more
  properties of the classes are input. This relies
  on the use of subsumption to check whether one
  class subsumes another.
• the OWLViz plugin can be used to visualise the
  class hierarchy of the developing DL theory.

34
Comparison Protégé

• While there are similarities with GIPO in the
  initial stages of domain (ontology) acquisition,
  Protégé-OWL lacked the facilities to 'execute'
  the model in any way.
• Students seemed to find GIPO more satisfying as
  they could build, view, validate and then execute
  the model.
• The ability to involve the model in some kind of
  constructive operation (ie plan generation)
  helped the student to see what the point of the
  knowledge acquisition process was.

35
Conclusions
36
Concluding Remarks

• Teaching KA/DM etc in AI is sometimes overlooked
  as it is hard enough teaching reasoning/representa
  tion
• Teaching KA/DM etc needs a tool!! But one which
  can be used to teach a wide area of the subject
  .. And one which allows the student to perform
  synthetic tasks
• Teaching KA/DM (with a tool such as GIPO) can
  help integrate parts of the computing curriculum

37
Some advertising