Download Components of KBS

Architecture of KBS
Components of KBS
Components of KBS
Notes
We have to build systems with specific characteristics:
Problem solving using symbolic information
Resolution by mean of reasoning and heuristic methods
Capacity to explain the resolution process
Interactivity (with a user/the environment)
Able to adapt to the environment
A basic set of components is needed
Reasoning subsystem
Knowledge storage subsystem (Knowledge base)
Knowledge use and interpretation subsystem (solving mechanism)
Problem state storage subsystem (working memory)
Explanation and resolution inspection subsystem
Communication and interface subsystem
Learning subsystem
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Components of KBS
Components of KBS
Notes
Reasoning subsystem
Knowledge
storage
Subsystem
Learning
subsystem
Explanation
and inspection
subsystem
Problem state
Knovledge
use and
interpretation
subsystem
storage
subsystem
Comunication and interface subsystem
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KBS based on production systems
KBS based on production systems
Notes
Problems are solved using the reasoning mechanism of an inference
engine
The domain knowledge is described by means of an ontology
The problem solving knowledge is described using production rules or
an equivalent formalism
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Architecture of KBS
KBS based on production systems
Knowledge storage subsystem
Notes
It will store all the knowledge needed to solve problems in the domain
of application
We can find three kinds of knowledge:
Factual knowledge (domain objects and their characteristics)
Relational knowledge (relations among the domain objects)
Conditional knowledge (Deductive knowledge about the problem)
The two first kind of knowledge are described in the domain ontology
The third one represents the knowledge related to the resolution of
the problem and is described by production rules
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KBS based on production systems
Knowledge storage subsystem: Rules
Notes
Conditional knowledge includes:
Deductive knowledge (structural): Describes the process of problem
solving as a chain of deductions
Knowledge about goals (strategic): Drives the resolution process to
the solution
Causal knowledge (supportive): Supports explanation of the problem
resolution process
Rule modules: Sets of related rules
Eases the development and maintaining of the system
Increases the efficiency of the reasoning process
Allows to implement strategies for the use of the knowledge
(meta-knowledge, meta-rules)
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KBS based on production systems
Knowledge storage subsystem: Meta-Rules
Notes
They describe high level knowledge about the process of solving the
problem
They allow to take control of the resolution process
Activating and deactivating rules/modules
Deciding the application order of rules/modules
Deciding resolution strategies, handling of exceptions, uncertainty, ...
This knowledge is more difficult to obtain from the experts
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Architecture of KBS
KBS based on production systems
Knowledge use and interpretation subsystem
Notes
Usually is an inference engine
It applies its execution cycle to solve the problem
Detection of applicable rules
Selection of the best rule (Domain independent strategy or driven by
metaknowledge)
Execution of the rule
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KBS based on production systems
Problem state storage subsystem
Notes
Stores the problem initial facts and all the facts obtained during the
resolution process
It can store also other kinds of information needed for the control of
the resolution process or other subsystems
Order of deduction of the facts
Use preferences of the facts
Rule that produced each fact
Rules recently fired
Backtracking points
...
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KBS based on production systems
Explanation and resolution inspection subsystem
Notes
The possibility to justify the decisions taken during the resolution
process gives credibility to the system
It allows also to detect wrong assumptions or decisions during the
process
A system should be able to answer why and how
Different levels of explanation:
Trace: A list of the steps of the resolution is given
Justification: The reason why each element that appear in the trace
of the resolution is given (reasoning path, questions, facts, preferences,
subgoals, ...)
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Architecture of KBS
KBS based on production systems
Learning subsystem
Notes
Usually the set of problems that a KBS solves is closed
In some domains it is necessary to adapt to the environment and to
solve new problems
Learning can happen:
During the development process of the KBS: The knowledge
engineering process is substituted or complemented by inductive
learning methods, building a model of the problem from examples
During the resolution process: Incorrect solutions are detected and
corrected, new rules that make more efficient the resolution process are
learned
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KBS based on Case Based Reasoning
Cased Based Reasoning (CBR)
Notes
The solution of a problem is obtained identifying a previous similar
solution
Advantages:
The problem of knowledge acquisition is reduced
It is easier to maintain/correct/extend the system
The solving process is more efficient
It allows to obtain explanations closer to the user experience
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KBS based on Case Based Reasoning
Execution cycle
Notes
It has four phases
1
Retrieval: The more similar stored cases are retrieved
2
Reuse: The solution of the cases are obtained
3
Revision: The retrieved solution is evaluated and adapted
4
Retention: The relevance of the new case is assessed and the case is
stored if it is an interesting one
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Architecture of KBS
KBS based on Case Based Reasoning
Execution cycle
Notes
Re
tri
e
va
l
New
Case
Case
Stored
Re
ten
tio
n
Case
Retrie
ved
New
Case
Cases
Domain
Knowledge
R
eu
se
Case
Revised
Revis
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ion
Case
Solved
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KBS based on Case Based Reasoning
Knowledge storage subsystem
Notes
The knowledge is composed by cases
A case is a complex structure (characteristics, solution)
The cases are stored in the case base (structure, indexing)
We will have also knowledge about:
How to evaluate case similarity
How to combine/adapt/retrieve solutions
How to evaluate solutions
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KBS based on Case Based Reasoning
Knowledge use and interpretation subsystem
Notes
It is based on the execution cycle of Cased Based Reasoning
Retrieve the more similar cases from the case base
Obtain the solution from the cases
Combine/adapt the solution (procedures/reasoning)
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Architecture of KBS
KBS based on Case Based Reasoning
Problem state storage subsystem
Notes
Information of the current case
Most similar cases retrieved
Reasoning results from evaluating/combining/adapting the solutions
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KBS based on Case Based Reasoning
Explanation - Learning
Notes
Explanation
It is a part of the information of a case
This explanation will be complemented by the reasoning process to
combine/adapt the solutions
Learning
To add new cases (easier than in production systems)
The new case has to be different enough (evaluation)
Cases could be forgotten (low usage, similar to others)
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Other methodologies
Other methodologies
Notes
Systems based on neural networks
Intelligent Agents/Multiagent systems
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Architecture of KBS
Other methodologies
Neural networks
Notes
Conexionist artificial intelligence
The base element is the neuron (computational element)
Neuron: Inputs, outputs, state, functions for the combination of
inputs and state and function to compute the output
Neurons are organized in networks with layers
A neural network associates inputs (problem description) to outputs
(solution of the problem)
A neural network has to be trained (using solved problems) in order
to learn how to solve the problem (association)
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Other methodologies
Neural networks
Notes
input1
weight1
s
weight2
f(inp1,...,wei1,...)
input2
weight3
input3
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Combination
Activation
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Other methodologies
Neural networks
Hidden Layers
Output
Layer
SOLUTION
EXAMPLES
Input
Layer
Notes
.
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Architecture of KBS
Other methodologies
Intelligent Agents/Multiagent systems
Notes
It supposes a collective vision of intelligent systems (instead of
monolithic)
An intelligent agent solves only a simple task
An agent:
Obtains information from the environment (peception)
Elaborates a decision based on its perceptions and state (reasoning)
Performs an action (actuation)
Agent
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Decision
Mechanism
Actuators
Perception
Action
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State
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Other methodologies
Intelligent Agents/Multiagent systems
Notes
The global problem is solved by cooperation/coordination
Techniques involved: organization, cooperation, coordination,
negotiation, tasks distribution, communication, reasoning about
others, ...
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Other methodologies
Intelligent Agents/Multiagent systems
Notes
Advantages:
More flexible systems
Reconfiguration/reorganization allow to solve other tasks
=⇒ Agents act as reusable components
Fault tolerance (an agent can be substituted by other)
Distributed computing
Related to:
Grid computing/Cloud computing (Management of tasks and
resources)
Web services (No reasoning capabilities)
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