Using GIPO to support learning in knowledge acquisition and automated planning

Lee McCluskey and Ron Simpson

ContentsContents 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

PART A:PART A:Some Problems with Teaching AISome Problems with Teaching AI

Problems with AI TeachingProblems with AI Teachingin practical workin practical work

We all know that areas such as ..knowledge representationautomated 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.

Problems with AI Teaching – Problems with AI Teaching – in in practical workpractical 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 ….

Problems with AI Teaching - Problems with AI Teaching - use of use of declarative programming declarative programming languages in AI practicalslanguages 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.

SummarySummary

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

PART B: using GIPO thePART B: using GIPO the 'Graphical Interface for 'Graphical Interface for Planning with Objects’Planning with Objects’

One possible solution.. Use of an One possible solution.. Use of an integrated tools environmentintegrated 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 haven’t time to learn many tools

relate AI to other subject areas taught at undergraduate level computing - students need the curriculum to fit together

GIPOGIPO

'Graphical Interface for Planning with Objects‘http://scom.hud.ac.uk/planform/gipois 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 (ICKEPS’05) in the general tools class at ICAPS 2005, Monterey, California

/

Knowledge RepresentationKnowledge 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…

Knowledge Acquisition / Knowledge Acquisition / FormulationFormulation

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

Domain AnalysisDomain 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.

Example: The Lazy Hiking WorldExample: The Lazy Hiking WorldImagine 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

Object Centric ViewObject 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

State Descriptors

TransitionDescriptors

Hiking Domain – ExampleHiking Domain – Example

TentProperty : LocationValue present in allidentified states.

Transition of Tent:Property Value ChangingLocation to Location

TransitionState Changing

TransitionProperty Value ChangingSatisfies next(x,y) nextStage(w,v)Constraint – NumberSatisfies couple(x,y)

Person

Car

Tent

PersonProperties: Location Stage

Transition Co-ordinationTransition Co-ordinationTransitions Requires Object at State

TransitionDependent on SourceBoth satisfy next(x,y)

Transitions mutuallydependentBoth satisfy next(x,y)

Add AssociationRecord car

Break AssociationForget Car

Libraries of Generic TypesLibraries 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

Richer RepresentationsRicher 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.

Dynamic TestingDynamic Testing Static Analysis may Indicate problems otherwise Manual Stepping may reveal

source of problem.

PART C: Student Learning PART C: Student Learning Experience with GIPOExperience with GIPO

Student LearningStudent Learning - Caveat - 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

Student LearningStudent 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.

Student Learning - methodStudent 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.

Student Learning – some Student Learning – some examplesexamples

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 GIPO’s 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.

Student LearningStudent Learning – connections – connections to other computing areasto 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

PART D: Comparison to other PART D: Comparison to other ‘similar’ tools used in UG ‘similar’ tools used in UG

computing teaching computing teaching

Comparison: The B-ToolComparison: 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.

Comparison: The B-ToolComparison: 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.

Comparison: The B-ToolComparison: 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.

Comparison: Comparison: ProtégéProtégé

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".

Protégé’s 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.

Comparison: Comparison: ProtégéProtégé

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.

Comparison: Comparison: ProtégéProtégé

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.

ConclusionsConclusions

Concluding RemarksConcluding Remarks

Teaching KA/DM etc in AI is sometimes overlooked as it is hard enough teaching reasoning/representation

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

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