the domain expert, • the knowledge engineer, • the programmer, • the

project manager and • the end-user.

Copilot
Sent by Copilot:

Sure! Here’s a brief explanation of each role:

1. Domain Expert: This person has deep knowledge and expertise in
the specific area or field the project is focused on. They provide
valuable insights and ensure the project meets industry standards and
requirements.
2. Knowledge Engineer: They are responsible for capturing and
structuring the knowledge from the domain expert. They create
models and frameworks that can be used by the system to solve
problems or make decisions.
3. Programmer: Also known as a developer, this person writes the code
to implement the system. They translate the models and frameworks
created by the knowledge engineer into a working software
application.
4. Project Manager: This person oversees the entire project, ensuring it
stays on track, within budget, and meets deadlines. They coordinate
between different team members and stakeholders to ensure smooth
progress.
5. End-User: The final user of the system or product. Their needs and
feedback are crucial as they determine the usability and success of the
project.
If you have any specific questions about these roles or need more
details, feel free to ask!
You
Sent by you:
Knowledge Engineering • The process of building intelligent
knowledge-based systems is called knowledge engineering. It has six
basic phases: 1. Problem assessment 2. Data and knowledge
acquisition 3. Development of a prototype system 4. Development of
a complete system 5. Evaluation and revision of the system 6.
Integration and maintenance of the system
Copilot
 Sent by Copilot:

Absolutely! Knowledge engineering is a systematic process for

creating intelligent systems. Let’s break down the six basic phases:
1. Problem Assessment: Identify and understand the problem that needs
to be solved. This involves defining the scope, objectives, and
constraints of the project.
2. Data and Knowledge Acquisition: Gather relevant data and
knowledge from various sources, including domain experts,
databases, and literature. This phase is crucial for building a solid
foundation for the system.
3. Development of a Prototype System: Create an initial version of the
system to test and validate the concepts. This prototype helps in
identifying potential issues and areas for improvement early in the
development process.
4. Development of a Complete System: Expand the prototype into a
fully functional system. This involves adding more features, refining
the system’s capabilities, and ensuring it meets all requirements.
5. Evaluation and Revision of the System: Test the system thoroughly
to ensure it works as intended. Gather feedback from users and
stakeholders, and make necessary revisions to improve performance
and usability.
6. Integration and Maintenance of the System: Deploy the system in
its intended environment and integrate it with existing systems if
necessary. Ongoing maintenance is essential to keep the system
updated and functioning correctly over time.
If you have any questions about these phases or need more details on
any specific part, feel free to ask!
You
Sent by you:
DENDRAL • First ES developed in late 1960 • Designed to analyze
mass spectra • Based on the mass of fragments seen in the spectra, it
would be possible to make inference as the nature of molecule tested,
identifying functional groups or even the entire molecule. •
identifying functional groups or even the entire molecule. • Used
Heuristic knowledge obtained from experienced chemists. • Use
forward chaining for reasoning Classic Expert Systems MYCIN •
 Expert system for treating blood infections • Diagnose patients based
on reported symptoms and medical test results • Could ask some more
information and lab test results for diagnosis • Recommend a course
of treatment, if requested, MYCIN would explain the reasoning that
lead to its diagnosis and recommendation. • Use about 500 production
rules, MYCIN operated roughly the same level of competence as
human specialists in blood infections. • Use backward chaining for
reasoning. Classic Expert Systems EMYCIN • It is an expert system
shell, a framework for building programs that record the knowledge
of domain experts and use that knowledge to help non-expert users
solve problems. • It provides an interface that helps experts define
data types and rules, a backward chaining reasoning algorithm, a
mechanism for dealing with uncertainty, and facilities for
introspection that permit users to learn what the system knows and
what it is doing. simle explain in understand in points
Copilot
Sent by Copilot:

Sure! Here are the key points for each expert system:
DENDRAL

 First Expert System: Developed in the late 1960s.

 Purpose: Analyzes mass spectra to infer the nature of molecules.
 Functionality: Identifies functional groups or entire molecules based
on mass fragments.
 Knowledge Source: Uses heuristic knowledge from experienced
chemists.
 Reasoning Method: Uses forward chaining.

MYCIN

 Purpose: Treats blood infections.

 Functionality: Diagnoses patients based on symptoms and medical
test results.
 Interaction: Can ask for more information and lab test results for
accurate diagnosis.
 Recommendations: Suggests treatment and explains the reasoning
behind its diagnosis.
 Knowledge Base: Uses about 500 production rules.
 Competence: Operates at the same level as human specialists in
blood infections.
 Reasoning Method: Uses backward chaining.

EMYCIN

 Type: Expert system shell (framework for building expert systems).

 Purpose: Helps non-expert users solve problems using domain expert
knowledge.
 Features:
o Interface for defining data types and rules.
o Backward chaining reasoning algorithm.
o Mechanism for dealing with uncertainty.
o Facilities for introspection (users can learn what the system knows
and is doing).

If you need more details or have any questions, feel free to ask!
You
Sent by you:
solve

Copilot
Analyzing the image: Privacy blur hides faces from Copilot
Sent by Copilot:

Sure! Here are the answers to the questions from the image:
1. Define task environment by rational agent:
o A task environment is the external setting in which an intelligent
agent operates. It includes everything the agent interacts with to
achieve its goals, such as sensors, actuators, and the environment
itself.
2. What do you mean by rational agent:
o A rational agent is one that acts to achieve the best possible outcome
or, when there is uncertainty, the best expected outcome based on its
knowledge and abilities.
3. What is constraint satisfaction problem?:
o A constraint satisfaction problem (CSP) is a problem where the goal
is to find a solution that satisfies a set of constraints or conditions.
Examples include scheduling tasks, solving puzzles, and resource
allocation.
4. Define conjunctive normal form with example:
o Conjunctive Normal Form (CNF) is a way of structuring logical
expressions where a formula is a conjunction (AND) of one or more
clauses, and each clause is a disjunction (OR) of literals. Example:
((A \lor \neg B) \land (B \lor C)).
5. What is the difference between fuzzy logic and bivalued logic?:
o Fuzzy Logic: Deals with reasoning that is approximate rather than
fixed and exact. It allows for degrees of truth.
o Bivalued Logic: Also known as classical logic, it deals with true or
false values only, with no in-between.
6. List any three activation functions in AI:
o Sigmoid
o ReLU (Rectified Linear Unit)
o Tanh (Hyperbolic Tangent)
7. Why NLP is important in AI?:
o Natural Language Processing (NLP) is important because it enables
machines to understand, interpret, and respond to human language,
making interactions with AI systems more natural and intuitive.
8. What is conflict set in production system:
o A conflict set is a collection of rules that are applicable at a given
point in the execution of a production system. The system must
choose one rule from this set to apply next.
9. What is uncertainty in AI?:
o Uncertainty in AI refers to situations where the system does not have
complete information or where outcomes are not deterministic. AI
systems use various methods, such as probability and fuzzy logic, to
handle uncertainty.