1. Specification and Knowledge Representation
CIS 488/588
Bruce R. Maxim
UM-Dearborn
11/16/2018

2. Specification
Seeks to find a way to represent the analysis concepts using formal notation or data structures
Dealing with specification explicitly is a huge advantage when AI needs to be scaled up
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3. Procedural View of AI
Consider the AI as a single procedure that needs to be passed information to return results or produce outputs
In C++ prototypes are used to describe function interfaces which describe the relationships among variables external to the function
At the end of the specification phase, an interface or scaffold is defined to describe how the AI fits into the rest of the system
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4. Types of Knowledge Objects Events Performance META Knowledge
both physical & concepts
Events
usually involve time
maybe cause & effect relationships
Performance
how to do things
META Knowledge
knowledge about how to use knowledge
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5. Stages of Knowledge Use - 1
Acquisition
structure of facts
integration of old & new knowledge
Retrieval (recall)
roles of linking and chunking
means of improving recall efficiency
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6. Stages of Knowledge Use - 2
Reasoning
Formal reasoning
deductive theorem proving
Procedural Reasoning
expert system
Reasoning by Analogy
very hard for machines
Generalization
reasoning from examples
Abstraction
simplification
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7. Knowledge Representation
Theory for expressing information in computer systems
The task of defining an interface is essentially KR
How should information passed to AI modules be encoded?
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8. Representation
Set of syntactic and semantic conventions which make it possible to describe things
Syntax
specific symbols allowed and rules allowed
Semantics
how meaning is associated with symbol arrangements allowed by syntax
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9. Knowledge Representation Issues
Grain size or resolution detail
Scope or domain
Modularity
Understandability
Explicit versus implicit knowledge
Procedural versus declarative knowledge
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10. Advantages Procedural representation Declarative representations
Easy to represent "how to do things"
Easy to represent any knowledge not fitting declarative format
Relatively easy to implement heuristic stuff on doing thing efficiently
Declarative representations
Store each fact once
Easy to add new facts
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11. Good Knowledge Representations
Important things made clear /explicit
Expose natural constraints
Must be complete
Are concise
Transparent (easily understood)
Information can be retrieved & stored quickly
Detail suppressed (can be found as needed)
Computable using existing procedures
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12. River Puzzle Problem Constraints
There are four items a farmer, wolf, goose, and corn. The farmer can only take one item across the river at a time.
Constraints
Wolf will eat the goose if left alone with it
Goose will eat the corn if left alone with it
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13. F=Farmer W=Wolf G=Goose C=Corn ~=River
W F
~ W
F G
F W F G ~ G F ~
W C W C C ~ G F
G ~ C F ~ W
C F ~ C F W W G
~ G G ~ G C C C
F C
W ~
G W
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14. Solution
Once graph is constructed finding solution is easy (simply find a path)
AI programs would rarely construct the entire graph explicitly before searching
AI programs would generate nodes as needed and restrict the path search to the nodes generated
May use forward reasoning (initial to goal) or backward reasoning (goal to initial)
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15. State Space Representation
How can individual objects and facts be represented?
How do you combine individual object descriptions to form a representation of the complete problem state?
How can the sequences of problem states that arise be represented efficiently?
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16. Attributes of Good KR Schemes -1
Representational Adequacy
works for all knowledge in problem domain
Inferential Adequacy
provides ability to manipulate structures to desire new structures
ability to incorporate additional information in knowledge structures to help focus attention of promising new directions
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17. Attributes of Good KR Schemes - 2
Acquisitional Efficiency
easy to add new knowledge
Semantic Power
Supports truth theory
Provides for constraint satisfaction
Can cope with incomplete or uncertain knowledge
Contains some commonsense reasoning capability
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18. KR Languages - 1 Provide means for formalizing representation
Expressiveness
How well language represents knowledge in general
Information can expressed in notational (explicit) form or inferential (deduced from existing knowledge) form
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19. KR Languages - 2 Efficiency tradeoffs
Disk storage limits the use of notational forms
Computational power limits the use of highly inferential forms
Inference support as basis for understanding
Well-defined syntax (sentence structure)
Well-defined semantics (sentence meanings)
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20. KR Language Attributes - 1
Consistent
Guarantees that statements are valid and conclusions drawn by systems are sound
Complete
How well does the language express the required knowledge?
Extensible
How easy is it to customize to particular problems?
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21. KR Language Attributes - 2
Natural
Easy to understand by humans (esp. domain experts)
Easy for humans to write when communicating with the computer
There is a strong link between reasoning and representation
However, most KR formalisms can be converted from one to another
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22. Representation Types Symbols Object-Attribute-Value (OAV)
Relational databases
Constraints
Predicate logic
Concept hierarchies
Semantic networks
Frames
Scripts
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23. Symbols Facts can be stored as text strings or numbers
This scheme can be implemented using any standard programming data types
The disadvantage is that every concept needs its own variable
Examples:
[left_obstacle_distance 4.0]
[right_obstacle “unknown”]
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24. OAV
Objects or concepts can have multiple variables associated with them
Implemented as C structs, C++ classes, Lisp property lists, Prolog predicates, database records,hash tables
Generally notated as A(O,V)
Examples:
distance(left_obstacle,4.0)
presence(right_obstacle,”unknown”)
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25. Semantic Networks
A declarative representation in which complex entities are described as collections of attributes and associated values
Sometimes called a “slot and filler” type structure
To assist in their implementation AI languages provide some type of associative memory in which object can be stored as OAV triples
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26. Decomposition
Most complex sets of objects can be decomposed into smaller subsets
These decompositions often contain two types of relations “isa” and “ispart”
dog isa pet isa animal isa living thing
finger ispart hand ispart body
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27. Inverse Relations
Sometimes it is also useful to define inverse relationships
“ako” (a kind of) as inverse of “isa”
“haspart” as inverse of “ispart”
dog ako pet ako animal ako living thing
finger haspart hand haspart body
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28. Animal Hierarchy
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29. Inheritance These relations form a partial ordering of the network
This allows us to use transitivity relations to aid in search
Storage of information is more efficient since inheritance can be used to “copy” information from a class to its subclasses
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30. Inheritance
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31. Value Inheritance Form a queue consisting of node F and
all class nodes found in F’s “isa” slot
Until queue is empty or value found
if queue front has value in slot S then
value found
else
remove first queue element and
add nodes related by “isa” slot
If value found then
report value found in slot S
announce failure.
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32. Semantic Nets
How do semantic networks differ from ordinary directed graphs?
In semantic networks there must be some underlying meeting associated with the representation (especially the edge or link labels)
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33. Semantic Network feathers wings bird eagle falcon has has isa isa
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34. Semantic Nets in C++ 1. bird 2. falcon 3. eagle 4. wings 5.feathers
has
2.falcon
isa
3.eagle
4.wings
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35. Problems with Semantic Nets
When do you have enough semantic primitives?
How do you know the selected primitives are correct?
What is the smallest number of link types needed to span all human knowledge?
How do you represent quantified knowledge?
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36. Frames
Can be looked at as being similar to a “pre-defined” semantic network
Frames contain information that can be used even if not observed
Frames contain attributes true of all instances of object or events
Frames contain stereotypical instances of objects or events
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37. Frame Attributes Based on stereotypes
Slot & filler type static representation
Make use of procedural attachment (demons) to fill in missing values
Allow us to use current explanation provided by frame until the “current view” is proven to be incorrect
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38. How are frame used?
People may select a frame from a list of proposed frames candidates based on a small amount of partial evidence (e.g. bigot)
The attributes of a selected frame are instantiated with observed attributes from the current object or event description
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39. How are frame used?
As values for slots are found they are copied to the evolving frame description
If slot values cannot be found or begin to contradict slot constraints a new frame candidate may need to be selected
You may also need to be alert for changes in the object or event while the frame is being instantiated
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40. What happens when frame instantiation fails?
You may be able to follow pre-defined links between frames in a frame system
bench
table
no back, too big
no back, too wide
drawers
chair
no back, too high
desk
stool
dresser
no kneehole
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41. What happens when frame instantiation fails?
Another option is to follow the inheritance links in the hierarchical structure formed by the frames (e.g. dog  mammal  animal) until a sufficiently “general” frame that does not conflict with the evidence is found
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42. Problems with Frames
Frames are not very frame-like (e.g. there are more atypical mammals than typical mammals)
Definitions are more important than most people think
Cancellation of default properties is very a tricky business
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43. Scripts
If frames can be viewed as using semantic networks to structure static information
Scripts can be viewed as using a series of related frames to represent dynamic information as a sequence a stereotypic events from a some context
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44. Script Attributes Entry conditions Result Props Roles Track Scenes
When does the script apply?
Result
What will be true once script is completed
Props
Roles
Track
Variation or specialization of usual script pattern
Scenes
Actual event sequences
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45. Restaurant Script Track: Coffee Shop
Props: Tables, menu, food (F), check, money
Roles: Customer (S), waiter (W), cook (C), casher (M), owner (O)
Entry conditions:
S is hungry, S has money
Results:
S has less money, O has more money, S not hungry, S happy (optional)
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46. Scripts
Are useful because they record patterns of the occurrence of events from the real world
These patterns are based on causal relationships between events (e.g. agents perform one action to be able to perform another action)
The sequence of script events define a causal chain that will facilitate reasoning about unobserved events
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47. Scripts
Can be used in question answering program (e.g. story comprehension)
Why did a waiter bring John a menu?
Scripts can also help to focus attention on unusual events as script departures
John went to a restaurant, was shown to a table, ordered a large steak waited for a long time, got angry, and left.
Why did John get angry?
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48. Scripts
When a script is known to be appropriate to a given situation it can be used to predicate the occurrence of future events
Example:
I went to a restaurant, ordered food, paid my bill, and left
Did I eat?
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49. Strengths of Scripts
Can be used to predicate events and answer questions
Provide a framework for integrating observations into a coherent interpretation
Scripts provide scheme for detecting unusual events
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50. Weaknesses of Scripts
Less general than frames so not appropriate for some knowledge types
If scripts can only account for all details in a restricted domain
It is unlikely that scripts can account for every real life scenario
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51. Introspection
For KR formalisms to have any impact they need to be implemented in such a way as to allow introspection
Introspection means that the AI is allowed to query knowledge properties dynamically
This may require the use of extra variables or data structures that can be examined during active game play
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52. Specification Phases Sketching Rationalization
Typical brainstorming activity
Ideas are collected and described informally
Rationalization
Ideas are checked for consistency with existing requirements
Valid ideas are formalized by converting the sketch into some KR
The KR is checked for consistency with requirements
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53. Role of Sketching - 1 Context Input
What goes on behind the interfaces that allows the problem to be solved?
Which variables are involved and how can their complexity be minimized?
Input
Select relevant inputs by asking “what info does human use in this case?
What problem variables for a programmer to solve this problem?
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54. Role of Sketching - 2 Output
How can a problem be decomposed into a sequence of actions?
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55. Inspiration Where do the idea for sketching come from?
Research (journals and conference proceedings)
Reading (look for developer reviews and game postmortems)
Relative work (look for open source projects to examine)
Borrowing idea (look for things that work in other domains)
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56. Formalizing Involves selecting a KR language
Determining data structures needed to implement the KR in the target programming language
Outputs are often easier to formalize than inputs (do this first)
Failing to create good formalisms for inputs is a frequent cause of failures later
The supporting variables from the context are often defined abstractly (they can be done last)
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57. Rationalizing
Does each model allow case studies to be satisfied and do they match the informal requirements?
How does model affect other requirements? (e.g. HW and SW)
Is the specification consistent with the rest of the design (interfaces and hierarchical dependencies)?
Are the interfaces flexible enough to handle all propose, applicable implementations?
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58. Specification Benefits
High-level understanding
Separates implementation and design, allowing focus to be on architecture rather than programming
Abstraction
Only need to be concerned with module inputs and outputs
Comparison
If interface remains constant alternative prototype implementations can be investigated
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59. Sir Tank
Uses straight forward reactive behaviors to bounce off of obstacles
Sir Tank’s behavior does not take orientation into account
This allows movement to be separated from other capabilities such as aiming
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