knowledge representation

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Knowledge Representation

• rule-based systems: those that use the knowledge structure

if premise(s) then conclusion(s)

• forward and backward chaining are the common inference strategies

• logical theorem proving is the general inference paradigm

- when KB is encoded as logical rules using AND, OR, NOT, implication, derive the logical truth or falsehood of some expression using the KB as a theory

- theorem proving is an active area in AI

- theoretical result: not all deductions are decideable; thus inference is a fundamentally complex and undecideable problem

• uncertainty also used

• Prolog: form conclusion :- premise, premise, ... conclusion.

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Knowledge Representation

Problems with rule-based systems

• multiple evaluation of rule (hence saving of user input)

• large rule sets: inefficient, maintainability

- 50 rule modules maximum

- if problem decomposition is difficult, perhaps change the knowledge representation (eg. add frames)

• uncertainty: choosing a technique, interpreting results

• misfit of problems to rule-based paradigm

- not all problems naturally suited

• procedural code in rules - forcing procedural execution (eg. Oops) can be messy

- Prolog hooks are a good solution

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Other techniques

i) Frames and object-oriented programming

ii) multiple contexts and truth-maintenance systems

iii) model-based representations

iv) blackboards

v) Case-based reasoning (CBR)

Rule of thumb:

When a knowledge base is difficult to understand and use,consider changing the representation.

Main goal: close the semantic gap between expert’s knowledge and computer implementation

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(i) Frames (review)

• knowledge structuring technique

• "frames": lmowledge representation exploiting structure & inheritence

"object-oriented programming": associating all data and computation with objects (= frames); computation done via message passing

• benefits:

- aids structuring of KB

- more generic rules, therefore reduce size, complexity of KB

- puts data in one area/module (a frame declaration)

- maintainability of KB

- objects, once identified, can contain characteristics, rather than spread this knowledge throughout many rules

- inference engine can more effectively use/modify the data

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Frames (cont)

• To use frames during knowledge engineering

i) decide on type of reasoning (forward, backward, other...)

ii) decide on tasks for rules and processing requirements

-iii) sketch a few rules, design a small hierarchy of frames, and test

• AI workstations have graphical frame interfaces

- permits quick definition, debugging

Problems with frames

• learning period for proper use and application

• efficiency: can be lots of execution time overhead - eg. one object change can entail 100's of subsidiary commands

• mis-use: create an inappropriate frame hierarchy/taxonomy - knowledge becomes difficult to understand - can also use too much procedural code, when declarative would be preferred

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Frames

p.250

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(ii) Multiple contexts

• context: single universe/world in which inference pertains

• some problems have different environments which result in different solutions

eg. time, season, path/direction, geographic location, ....

• multiple context expert system: performs separate inferences for different contexts, using the same ruleset

- otherwise, one inference and rule set to do the same

- complicated KB and rules, that need to reconcile different contexts - inefficient: too much inferencing and backtracking

• one can have multiple "parallel" inferences for all the contexts; then one can inspect the results for each and determine which is best alternative

- each uses identical KB rules, but may require their own environments (variable binding records, run-time databases, ....)

• akin to parallel processing instead of backtracking

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Multiple contexts

p.256

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Multiple contexts

p.257

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Multiple contexts

• Applying multiple contexts

1. selecting context structure for problem

- problem specific - often hierarchical structure are well-suited

2. Reasoning with contexts: "fit" of context to problem <---> reasoning

(i) parallel reasoning: mutually exclusive sub-problems

(ii) pruning: trim computation space (like a "cut") - else resources can be overwhelmed - dominance: once one context is known to be best, then kill others

(iii) merging: when 2 contexts have identical facts and assumptions, incorporate them together


Truth maintenance systems

• monotonic reasoning: set of beliefs grow as inference proceeds

non-monotonic reasoning: adding contexts can reduce belief set (eg. by retracting facts)

• Truth maintenance systems (TMS): (consistency mgmt. systems)

- when one assumption changes, all conclusions from that assumption must be retracted (repeated)

• Three types of TMS:

i) Justification TMS: simplest - set of beliefs in assumption set is monotonic, never shrinks - facts are either believed or not; but not believing a fact is not the same as believing it is false

ii) Logic-based TMS - each predicate is true, false, unknown; represented in logic - contradictions are detected efficiently

iii) Assumption-based TMS - conexts used in parallel (as in previous overheads)


TMS

p.266


TMS

p.267


Multiple contexts

• Searching multiple contexts: a tree of all possible inferences

(i) breadth-first: each level is generated one-by-one, fairly - complete, but resource expensive

(ii) depth-first: Prolog technique - efficient, but possibly non-terminating

(iii) controlled: more arbitrary, can move about tree as required

"general theorem proving"


(iii) Model-based representation

• encode knowledge in a structure which naturally describes it• use: realtime monitoring and diagnosis of machinery - can reason about common and uncommon failures

(i) Static models

• graph, node & arcs

• good for representing connection paths between components/modules/...

supply input: forward chaiing supply output: backward chaining

• benefits:

- clarity: can permit visual representation - a direct simulation of object of interest - familiarity: permits better debugging of KB - maintenance: change components in model, rather than KB rules - simplification: KB is simpler - efficiency: compact representation of domain, smaller than with rules/facts


Static models

p.289


Static models

p.291


Model-based representation

(ii) dynamic models:

• the state of the model changes over time, and this state change is the phenomemon of interest

• simulation language/systems

• supply inputs, system generates output, which is simulated dynamic behavior of whole system

• used when actual domain unavailable, therefore a formal model is used as a substitute

eg. nuclear power plant, earth's ozone layer, aircraft modelling, ...

• used in many AI systems (eg. KEE)

• benefit: can ascertain data that would normally have to be treated as UNKNOWN


(iv) Blackboards

• blackboard: representation of knowledge with a high-level controlling element

• first used in Hearsay II

• components:

knowledge sources (KS) : modules with specific expertise (tests, actions) blackboard: knowledge storage and communication control: overall problem-solving strategy

• some control strategies

- event-driven: react to events

- expectation driven: predict solution based on current BB state, and act appropriately

- request driven: control given to the KS which seems appropriate to a particular request

- goal directed: control given to KS likely to solve some goal


Blackboards

p.304


Blackboards

•Some conditions for BB use•static knowledge sources: KS's don't change structure (BB does)

•decomposable knowledge: get a small number of complex KS's•independence of KS's: run independently without hooks to one another• - BB is a medium that buffers them•hierarchical problems: layer problem domain• - each level taken care of by one or more KS's


Blackboard characteristics

• Diverse approaches to problem solving

– modular, independent

• Common language for interaction

– formal interfaces

• Flexible representation of information

• Efficient storage and retrieval of information

– permits KS’s to inspect BB, and retrieve info as necessary

• Organized participation

– a distributed computing problem

• Iterative problem solving

– KS’s contribute to solution incrementally


Blackboards

• Some conditions for BB use

– static knowledge sources: KS's don't change structure (BB does)

– decomposable knowledge: get a small number of complex KS's

– independence of KS's: run independently without hooks to one another• BB is a medium that buffers them

– hierarchical problems: layer problem domain• each level taken care of by one or more KS's


Blackboards

p.315


Blackboards

When to use BB's

• real-time processing (speach, signal processing)

• some scheduling and planning applications

• problems which naturally decompose into separate independent tasks

• problems which are best handled procedurally

- this is also a pitfall, as BB and procedures can be applied to problems for which declarative rule-based methods would be perfectly suited


(v) Case-based Reasoning (CBR)

• Case: operational description of a past problem and its solution• CBR idea: experts use vast amount of past experiences to derive new solutions

– their compiled knowledge often uses pattern matching on long-past experiences

• CBR records and documents a history of past cases– then attempts to match a given program with closest past case– the more the CBR system is used, the larger it’s case history becomes, and

the more effective it can be• ideal when a problem is difficult to formulate in terms of explicit rule-based

knowledge– often, the closest case matching a new problem is sufficient for correctly

diagnosing the problem• Issue: newer cases are usually more pertinent than older ones

– CBR system should account for case ages• Note: inductive inference (ID3) is one style of CBR

– each example in table is a case

knowledge representation

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