Knowledge Representation - Embedded Intelligent Robotics - Lecture Slides, Slides of Robotics

This course is about robots intelligence. This lecture is one of many lectures on robots you can find in my uploads. Following key points are hint to specific topics of this lecture. Knowledge Representation, Syntactic, Semantic Conventions, Specific Symbols, Syntax, Semantics, Arrangements, Relational Databases, Constraints, Predicate Logic

Typology: Slides

2012/2013

Uploaded on 03/17/2013

salman
salman 🇮🇳

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Knowledge Representation

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

Types of Knowledge

  • Objects
    • both physical & concepts
  • Events
    • usually involve time
    • maybe cause & effect relationships
  • Performance
    • how to do things
  • META Knowledge
    • knowledge about how to use knowledge

Stages of Knowledge Use

  • Acquisition
    • structure of facts
    • integration of old & new knowledge
  • Retrieval (recall)
    • roles of linking and chunking
    • means of improving recall efficiency

Knowledge Representation Issues

  • Grain size or resolution detail
  • Scope or domain
  • Modularity
  • Understandability
  • Explicit versus implicit knowledge
  • Procedural versus declarative knowledge

Advantages

  • Declarative representation
    • Store each fact once
    • Easy to add new facts
  • Procedural representation
    • 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

Attributes of Good KR Schemes

  • 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

Broad KR Questions

  • Are there properties of objects so basic that they occur in every domain?
  • If so what are they?
  • At what level should knowledge be represented?
  • Is there a good set of primitives into which all knowledge can be broken down?
  • How can the relevant parts of a large knowledge base be accessed when needed?

Two Approaches

  • Use complete object descriptions that include

relations to other objects in the environment

  • Use predicate logic to express these kind of

relations

on(plant,table). under(table,window). in(table,room).

Frame Problem

  • What (or how much) should be stored at each node?
  • How do you distinguish between facts that change from facts that do not change between frames?
  • Stated and another way, how do you decide how much information to record as you move from problem state to problem state?

Frame Problem

  • Better solution is to not physically modify the state description but merely record a list of changes that should be made at this node
  • To get to the current state, you start at the initial state and then apply the changes recorded
  • Backtracking will be easy, but state description is complicated

Frame Problem

  • Another alternative would be to modify the

state description but mark the changes made

so they can be undone when backtracking is

required

  • If temporal relations (time) are involved things

will become regardless of the approach used