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16 April 2011 Alan, Edison, etc , Saturday. Knowledge, Planning and Robotics. Knowledge Types of knowledge Representation of knowledge Planning Knowledge for planning Planning in robotics Logic in robot planning and behavior. Knowledge Representation. Representational adequacy
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16 April 2011 • Alan, Edison, etc , Saturday.
Knowledge, Planning and Robotics • Knowledge • Types of knowledge • Representation of knowledge • Planning • Knowledge for planning • Planning in robotics • Logic in robot planning and behavior
Knowledge Representation • Representational adequacy • declarative, procedural • Inferential adequacy • manipulate knowledge • incorporate new knowledge
Types of Knowledge • Simple facts • Complex organized knowledge • procedure - how to knowledge • meta-knowledge
Semantic Data Models • High level model of model • Model of conceptual model • Not tied to implementation concerns • Focus on • expressiveness • simplicity • concise • formality
Semantic Nets • Nodes represent Objects • Links or Arcs represent Relationships • “instance of” - set membership • “is a” - inheritance • “ has a” - attribute descriptors • “part of” - aggregation
Is a Has a Part-of Instance of
Semantic NetsAdvantages Disadvantages • Flexible • easy to understand • support inheritance • “natural” way to represent knowledge • Hard to deal with exceptions • procedural knowledge difficult to represent • no standards for defining nodes or relationships
Classes, Objects, Attributes, Values - Object Orientation • Classes describe common properties of objects • Objects may be physical or conceptual • Attributes are characteristics of objects • Values are specific measures of Attributes for specific instances
Classes • Specify common properties of instances • support hierarchical classification • superclass / subclass • subclass may be more refined version • each subclass inherits operations and attributes of its ancestors • subclass may have its own operations and attributes
Objects or Instances • Refers to things identified in model of conceptual model • may be tangible (equipment, part, orders, squashed bananas) • may be mental constructs
Class vs instances Person class instances
Inheritance • Inheritance is sharingattributes and behaviors within a class of objects Person Employee Sales Person Manager customer Sale Manager
Encapsulation • Attributes and behaviors (methods) integrated with the classes and objects Attributes: size, location, appearance Methods
Polymorphism • Each object responds in its unique way to messages When changed method When needed method
Object-Orientation • Tool for managing complexity • emphasis on object structure • specify “what is” • mapped directly from semantic net
Rule Representations • Rules are called productions • Rule have two parts • condition part, premise -> IF • action part ,conclusion-> THEN • The action can: • add a fact to the knowledge base, • start a procedure • or display a screen
Rules represent knowledge • Apply O-A-V framework (object-attribute-value) • IF air vehicle is a plane AND plane maximum altitude is 40000 AND plane manufacturer is Boeing THEN ASK Flight Display 15
Representing knowledge • Abstracting with rules • translate quantitative to qualitative • define technical terms • support generalized reasoning • make rules for user • easy to understand • help user follow decision logic
Rule for understanding • Quantitative to Qualitative • qualitative language is easier to understand • interpretation of numerical data • make user feel comfortable with decision logic • If temperature > 200 and humidity is 85% then machine is slightly overheated
Definitional Rules • Help communicate and train users • Help user understand vocabulary • Promotes common agreement on terms for expert, user and knowledge engineer • IF you want more than one source file of classes THEN use package keyword
Rules support Generalizations • Allow reasoning with from specialization to generalizations • Support classification of objects at higher levels • Support refinements
Surface Knowledge Hard to understand Difficult to learn reasoning strategies hard to update and expand knowledge base If pump operation temperature is over 300 AND water mixture pH > 5.2 THEN replace pump bearing and oil
Hierarchical Classification Abstraction draws out important aspects Solution abstractions Feature abstractions Heuristic Match generalize refine Features Recommendations
Deep knowledge Lubrication defect Is a Poor Oil Viscosity causes causes Hot Pump Low Temp temperature is over 300 water mixture pH > 5.2
Reasoning at higher level requires Lubrication defect Maintenance Type of Fix heat damage Remedy Replace bearing and oil
Rules Advantages Disadvantages • Modular style - easy to add, update and delete • natural for many problem domains • uncertain knowledge may be represented • May be difficult to understand • may demonstrate unpredictable behavior • extra effort required to representing structural knowledge
Predicate Logic • Programming by description • describe the problem’s facts • built in inference engine combines and uses facts and rules to make inferences
Prolog Programming • Declaring facts about objects and their relationships -> likes (john,mary) • Defining rules about objects and relationships • Asking Questions about objects sister-of(X,Y) :- female(X), parents(X,M,F), parent(Y,M,F)
Frames • Similar to objects • helps organize entities • packages operations (demons) • easy to modify • extensible through inheritance
Frame - natural representation • Can accommodate a taxonomy of knowledge • contains defaults expectations • represent procedural and declarative knowledge