artificial intelligence that covers frames

1)What is procedural knowledge. Explain it with an example.
In the context of AI, procedural knowledge refers to knowledge about how to perform
specific tasks or actions using algorithms, methods, or procedures. It involves
understanding the step-by-step processes or sequences of actions required to achieve a
particular goal in the domain of artificial intelligence. Procedural knowledge is often
associated with the execution of algorithms and the implementation of specific techniques
to solve problems. Let's explore procedural knowledge in AI with an example:
Example: Procedural Knowledge in Image Classification
Consider the task of image classification, where the goal is to develop a system that can
identify and categorize objects in images. Procedural knowledge in this context involves
understanding the step-by-step processes and algorithms to create an image classification
system. Here's a simplified example:
1.Data Collection:
1. Procedural knowledge includes knowing how to collect a diverse and representative
dataset of images for training the classification model.
2.Preprocessing:
1. Knowing how to preprocess the images is part of procedural knowledge. This
involves tasks such as resizing images, normalizing pixel values, and augmenting
the dataset for better generalization.
3.Model Selection:
1. Procedural knowledge involves choosing a suitable machine learning model
architecture for image classification, such as a Convolutional Neural Network
(CNN).

1.Model Training:
1. Knowing how to train the selected model using the prepared dataset is a crucial aspect.
Procedural knowledge includes understanding the training process, loss functions, and
optimization techniques.
2.Hyperparameter Tuning:
1. Procedural knowledge may include tuning hyperparameters, such as learning rates or
batch sizes, to optimize the performance of the model.
3.Evaluation:
1. Knowing how to evaluate the trained model on a separate test dataset is part of procedural
knowledge. This involves using metrics like accuracy, precision, recall, or F1 score.
4.Deployment:
1. Procedural knowledge extends to deploying the trained model for real-world applications.
This includes integrating the model into a software system or creating an API for
predictions.
5.Continuous Improvement:
1. Procedural knowledge also involves understanding how to iteratively improve the model.
This may include retraining with new data, updating the model architecture, or fine-tuning
parameters.

2)What is Relational knowledge. Explain it with an example
Relational knowledge refers to information or understanding about the relationships and
connections between different entities or concepts. In the context of artificial intelligence, this
type of knowledge involves recognizing and representing how various elements in a system are
related to one another. Relational knowledge is crucial for reasoning, making connections, and
drawing inferences based on the relationships within a given domain. Let's explore relational
knowledge with an example:
Example: Relational Knowledge in a Social Network
Consider a social network scenario where individuals are connected through friendships.
Relational knowledge in this context involves understanding the connections and relationships
between different people. Here's an example:
1.Entities:
1. Identify entities in the system, such as individuals or users in a social network. Let's
consider individuals named Alice, Bob, and Carol.
2.Relationships:
1. Define relationships between these entities. In this example, the relationship is
friendship.
3.Knowledge Representation:
1. Represent the relational knowledge in a knowledge base. For instance:
1. Alice is friends with Bob.
2. Bob is friends with Carol.

1.Inference:
1. With relational knowledge, the system can infer additional relationships or make
predictions. For example:
1. If Alice is friends with Bob and Bob is friends with Carol, the system can infer that
Alice might indirectly know Carol through the transitive property of friendship.
2.Recommendations:
1. Relational knowledge enables the system to make personalized recommendations. If
Alice and Bob have common friends, the system might suggest new connections for
Alice based on Bob's network.
3.Analysis:
1. Analyzing the network structure based on relational knowledge can reveal patterns,
such as identifying central individuals who connect different clusters of friends.
4.Updates:
1. Relational knowledge can be updated dynamically as new connections are formed or
existing relationships change. For example, if Alice and Bob become unfriended, the
system updates the relational knowledge accordingly

3)What are frames and scripts in AI?
Frames and scripts are two concepts in artificial intelligence that are used to represent
and organize knowledge in a structured manner. They provide a way to model and
store information about objects, events, and concepts in a way that reflects their
hierarchical and relational nature.
1.Frames:
1. Definition: A frame is a data structure used to represent a stereotypical
situation, object, or concept. It consists of attributes and values associated with
those attributes. Frames help organize knowledge hierarchically and capture the
properties and relationships of entities within a particular domain.
2. Components:
1. Slot: Each attribute-value pair within a frame is often referred to as a slot.
Slots represent specific features or characteristics of the frame.
2. Filler: The value associated with a slot is called a filler. It provides
information about the attribute.
3. Example:
1. Consider a "Car" frame with slots like "Color," "Model," and "Manufacturer."
The frame might have fillers like "Red," "Sedan," and "Toyota," respectively.

1.Scripts:
1. Definition: A script is a structured representation of knowledge about a particular event, activity, or
scenario. It captures the expected sequence of actions or events that typically occur in a given
situation. Scripts help in understanding and predicting the flow of events in familiar contexts.
2. Components:
1. Nodes: Events or actions are represented as nodes in a script.
2. Links: Links connect nodes and represent the temporal or causal relationships between
events.
3. Example:
1. Consider a "Restaurant" script with nodes like "Customer enters," "Orders food," "Eats," and
"Pays." Links between these nodes indicate the expected order of events in a typical restaurant
scenario.
Key Differences:
•Focus:
• Frames focus on organizing information about objects and concepts.
• Scripts focus on organizing information about events, activities, or scenarios.
•Representation:
• Frames use attributes (slots) and values (fillers) to represent information about entities.
• Scripts use nodes and links to represent the sequence and relationships between events.
•Application:
• Frames are often used to represent static knowledge about entities in a domain.
• Scripts are used to model dynamic knowledge about the sequence of events in a particular
situation.
•Example:
• A "Car" frame might represent information about a specific car, including its color, model, and

Knowledge representation in artificial intelligence involves capturing and organizing
information in a form that allows systems to reason, learn, and solve problems. Several
approaches to knowledge representation techniques have been developed.
What are the different approaches of Knowledge Representation Techniques?
1.Semantic Networks:
1. Description: Semantic networks represent knowledge using nodes (concepts) and
arcs (relationships). Nodes denote entities or concepts, and arcs indicate
relationships between them. This approach is visually intuitive and is suitable for
representing hierarchical relationships.
2.Frames:
1. Description: Frames are data structures that organize knowledge about a concept in
terms of attributes and values. Each attribute-value pair is called a slot, and frames
are used to represent stereotypical situations, objects, or concepts. This approach is
effective for capturing structured information.
3.Rules-based Representation:
1. Description: Knowledge can be represented using a set of rules in the form of
condition-action pairs (if-then statements). Rules-based representation allows
systems to make inferences and decisions based on a set of predefined rules. Expert
systems often use this approach.

4.Predicate Logic:
1. Description: Predicate logic represents knowledge using first-order logic statements. It
employs predicates, variables, and quantifiers to express relationships and constraints. This
approach is formal and is widely used in symbolic reasoning systems.
5.Ontologies:
1. Description: Ontologies define a shared and explicit conceptualization of a domain. They
represent knowledge in terms of classes, properties, and relationships. Ontologies provide a
common vocabulary for knowledge sharing and interoperability in various applications.
6.Fuzzy Logic:
1. Description: Fuzzy logic allows for the representation of uncertainty and imprecision in
knowledge. It extends classical logic by introducing degrees of truth between 0 and 1. Fuzzy
logic is particularly useful in situations where information is not binary but exists on a
continuum.
7.Neural Networks:
1. Description: Neural networks represent knowledge through interconnected nodes inspired by
the structure of the human brain. They learn patterns and relationships from data, making them
suitable for tasks like pattern recognition and machine learning.
8.Probabilistic Models:
1. Description: Probabilistic models, such as Bayesian networks, represent knowledge using
probabilities to model uncertainty. They are effective for reasoning under uncertainty and
making decisions based on probabilistic inference.
9.Conceptual Graphs:
1. Description: Conceptual graphs provide a graphical representation for expressing knowledge.
They combine elements of semantic networks and predicate logic, using nodes and labeled

10.Production Systems:
1. Description: Production systems consist of rules organized in a rule base. The rules are
evaluated using an inference engine, and actions are triggered based on the conditions
specified in the rules. Production systems are widely used in expert systems and decision
support systems.
11.Description Logics:
1. Description: Description logics extend propositional logic by allowing the representation of
complex concepts and relationships. They are used in knowledge representation for the
semantic web and ontological modeling.
5)How do you represent visiting a theater in the form of a Script? Explain.
Representing the activity of visiting a theater in the form of a script involves outlining the
sequence of events and actions that typically occur during this particular scenario. A script
captures the expected flow of events, providing a structured representation of the activity.
Here's an example of how you might represent visiting a theater as a script:
Script: Visiting a Theater
1.Node: Arrival at the Theater
1. Action: Individual arrives at the theater.
2.Node: Ticket Purchase
1. Action: Individual approaches the ticket counter.
2. Action: Individual purchases a ticket for the desired movie and showtime.
3.Node: Entry to the Theater Lobby
1. Action: Individual proceeds to the theater lobby.

1.Node: Snack Purchase
1. Action: Individual may choose to purchase snacks from the concession stand.
2. Action: Individual pays for the snacks.
2.Node: Ticket Verification
1. Action: Individual presents the ticket for verification at the entry point.
2. Action: Theater staff verifies the ticket.
3.Node: Finding the Seat
1. Action: Individual locates the assigned seat in the theater.
4.Node: Seating
1. Action: Individual takes a seat and prepares for the movie.
5.Node: Enjoying the Movie
1. Action: Individual enjoys the movie experience.
6.Node: Post-Movie
1. Action: After the movie, individual may choose to exit immediately or stay for credits.
2. Action: If staying, individual remains seated until the end of the credits.
7.Node: Exit from the Theater
1. Action: Individual exits the theater after the movie concludes.
2. Action: If applicable, individual disposes of any trash in designated bins.

6)What is semantic net? Explain it with an Example.
A semantic network, also known as a semantic net, is a graphical representation used to
depict relationships and connections between concepts or entities in a domain. It is a type
of knowledge representation structure that uses nodes to represent concepts and directed
arcs (edges) to represent relationships between these concepts. Semantic networks are
often employed in artificial intelligence and cognitive science to model and organize
knowledge. Let's explore the concept of a semantic net with a simple example:
Example: Semantic Net for Animal Relationships
Consider a semantic network representing relationships between different animals:
1.Nodes:
1. Each type of animal is represented by a node. For simplicity, let's consider nodes for
"Dog," "Cat," "Bird," and "Fish."
2.Arcs (Edges):
1. The relationships between animals are represented by directed arcs. Here are some
examples:
1. An arc labeled "IsA" from "Dog" to "Mammal" indicates that a dog is a mammal.
2. An arc labeled "IsA" from "Cat" to "Mammal" indicates that a cat is a mammal.
3. An arc labeled "Can" from "Bird" to "Fly" indicates that a bird can fly.
4. An arc labeled "IsA" from "Fish" to "Aquatic" indicates that a fish is aquatic.

1.Querying the Semantic Net:
1. With this representation, one can easily query the semantic net. For example:
1. "What are the mammals?" The answer is "Dog" and "Cat."
2. "Which animals can fly?" The answer is "Bird."
2.Use Cases:
1. Semantic nets are used in various AI applications for knowledge representation, natural
language understanding, and reasoning. They provide an intuitive and visual way to model
relationships within a specific domain.
Semantic networks can be extended to include more complex relationships, attributes, or
hierarchies, making them versatile tools for representing structured knowledge. The example
above illustrates a simplified semantic net for animal relationships, but in practice, semantic nets
can capture more intricate information and connections within diverse domains.
7)List the advantages and disadvantages of Semantic Networks.
A semantic network, also known as a semantic net, is a graphical representation used to
depict relationships and connections between concepts or entities in a domain. It is a type
of knowledge representation structure that uses nodes to represent concepts and directed
arcs (edges) to represent relationships between these concepts. Semantic networks are
often employed in artificial intelligence and cognitive science to model and organize
knowledge.

artificial intelligence that covers frames

8)For the following knowledge, construct semantic network.
i. Every human, animal & birds are living things who can breathe & eat.
ii. All birds can fly.
iii. Every man & woman are human who have 2 legs.
iv. A cat has fur & is an animal.
v. All animals have skin & can move.
vi. A giraffe is an animal & has long legs & is tall.
vii. A parrot is a bird & is green in colour.

9)Explain about Extended Semantic Networks for KR
Extended Semantic Networks (ESNs) represent an advancement in traditional semantic
networks, enhancing their capabilities for knowledge representation (KR). ESNs
incorporate additional features and mechanisms to address limitations and to make
them more suitable for complex domains. Here are some key aspects of Extended
Semantic Networks:
1.Attribute-Value Pairs:
1. ESNs often include the representation of attribute-value pairs associated with
concepts. This allows for a more detailed specification of properties and
characteristics related to each concept in the network.
2.Slots and Fillers:
1. Concepts in ESNs may have slots that represent specific attributes or
properties, and these slots are filled with corresponding values. This structured
approach enhances the ability to capture detailed information about entities.
3.Frames:
1. ESNs may incorporate the concept of frames, which are structured units
containing attributes, values, and relationships. Frames provide a way to
organize and represent complex information about individual entities or
concepts.
4.Inheritance Mechanisms:
1. ESNs often include mechanisms for representing inheritance, allowing for the
propagation of attributes and relationships from more general concepts to more
specific ones. This facilitates the modeling of hierarchical structures and

1.Temporal Representation:
1. ESNs can be extended to represent temporal aspects of knowledge, allowing for the
modeling of events, states, or changes over time. This is particularly useful in domains
where time plays a crucial role.
2.Uncertainty Handling:
1. Some ESNs incorporate mechanisms to handle uncertainty in knowledge representation.
This includes representing probabilistic information, degrees of belief, or fuzzy logic to
express imprecise or uncertain knowledge.
3.Semantic Relationships:
1. ESNs emphasize semantic relationships between concepts, providing a foundation for
representing the meaning and context of relationships. This enables a more nuanced
understanding of the knowledge being represented.
4.Integration with Rule-Based Systems:
1. ESNs can be integrated with rule-based systems, allowing for the incorporation of logical
rules that govern the behavior and reasoning within the knowledge representation system.
This combination enhances the expressive power of ESNs.
5.Knowledge Inference:
1. ESNs may support more advanced knowledge inference mechanisms, enabling the
system to deduce new information based on the existing knowledge structure. This
contributes to the system's reasoning capabilities.
6.Machine Readability:
1. ESNs may incorporate features to enhance machine readability, making it easier for
automated systems to process and interpret the knowledge represented in the network.

10)Develop a complete frame-based system for hospital application
Designing a complete frame-based system for a hospital application involves creating structured
frames that represent various entities, attributes, and relationships within the hospital domain. Below is
a simplified example of a frame-based system for a hospital application. Keep in mind that a real-world
hospital system would likely be more complex and comprehensive.
Frame: Patient
•Attributes:
• Patient ID
• Name
• Age
• Gender
• Address
• Contact Number
• Admission Date
• Discharge Date
• Medical History
• Allergies
• Emergency Contact
•Relationships:
• Admitted in Ward (Links to Ward frame)
• Assigned Doctor (Links to Doctor frame)
• Scheduled Procedures (Links to Procedure frame)

Frame: Doctor
•Attributes:
• Doctor ID
• Name
• Specialization
• Contact Number
• Schedule (Working hours)
• Patient List (Patients currently under care)
•Relationships:
• Works in Department (Links to Department frame)
• Performs Procedures (Links to Procedure frame)
Frame: Department
•Attributes:
• Department ID
• Department Name
• Head of Department (Links to Doctor frame)
• List of Doctors (Links to Doctor frames)
•Relationships:
• Contains Wards (Links to Ward frames)
Frame: Ward
•Attributes:
• Ward ID
• Ward Type (e.g., General, ICU, Pediatric)
• Capacity
• Occupancy

• Medication Name
• Dosage
• Prescription Date
• Prescribing Doctor (Links to Doctor frame)
• Patient Prescription (Links to Patient frame)
Frame: Billing
•Attributes:
• Bill ID
• Patient (Links to Patient frame)
• Total Cost
• Breakdown of Costs (e.g., Procedures, Medications)
Frame: Appointment
•Attributes:
• Appointment ID
• Patient (Links to Patient frame)
• Doctor (Links to Doctor frame)
• Appointment Date and Time
Frame: Emergency
•Attributes:
• Emergency ID
• Reporting Staff (e.g., Nurse or Receptionist)
• Emergency Type
• Description
• Response Time
• Involved Staff (e.g., Doctors, Nurses)

Explanation:
1.Patient Frame:
1. Contains attributes such as Patient ID, Name, Age, etc.
2. Relationships with Ward, Doctor, and Procedure frames.
2.Doctor Frame:
1. Contains attributes like Doctor ID, Name, Specialization, etc.
2. Relationships with Department and Procedure frames.
3.Department Frame:
1. Includes attributes Department ID, Department Name, etc.
2. Relationships with Doctor and Ward frames.
4.Ward Frame:
1. Attributes include Ward ID, Ward Type, Capacity, etc.
2. Relationships with Department and Patient frames.
5.Procedure Frame:
1. Attributes include Procedure ID, Procedure Name, etc.
2. Relationships with Doctor, Department, and Medication frames.
6.Nurse Frame:
1. Contains attributes like Nurse ID, Name, Shift Schedule, etc.
2. Relationships with Ward frame.
7.Medication Frame:
1. Attributes include Medication ID, Medication Name, Dosage, etc.
2. Relationships with Procedure and Patient frames.
8.Billing Frame:
1. Includes attributes Bill ID, Patient, Total Cost, etc.
2. Relationships with Patient frame.

11.Write about Conceptual Dependency Theory. How it will be used for
Knowledge Representation
1.Appointment Frame:
1. Contains attributes Appointment ID, Patient, Doctor, etc.
2. Relationships with Patient and Doctor frames.
2.Emergency Frame:
1. Attributes include Emergency ID, Reporting Staff, Emergency Type, etc.
2. Relationships with Patient and Staff frames.
Conceptual Dependency (CD) Theory is a knowledge representation framework introduced by
Roger Schank and Robert Abelson in the 1970s. It is designed to model human cognitive
processes and provide a structured way to represent knowledge about actions, events, and
concepts. Conceptual Dependency Theory is primarily focused on capturing the meaning and
relationships between different elements of knowledge.
Key Components of Conceptual Dependency Theory:
1.Primitives:
1. CD Theory defines a set of primitives, which are basic conceptual elements
representing actions, objects, and relationships. Examples include "Action,"
"Object," "Agent," and "Patient."
2.Conceptual Graphs:
1. Knowledge is represented in the form of conceptual graphs, which are graphical
structures depicting the relationships between concepts. These graphs consist of
nodes representing concepts and arcs indicating relationships or actions.

1.Case Structures:
1. Case structures represent specific instances of actions or events. Each case
structure consists of slots corresponding to the various conceptual primitives,
providing a way to fill in details about a particular situation.
2.Semantic Roles:
1. CD Theory introduces semantic roles such as "Agent," "Patient," and "Instrument" to
define the roles played by different elements in a given action or event. These roles
contribute to the understanding of the meaning of a statement.
How Conceptual Dependency Theory is Used for Knowledge Representation:
1.Representation of Actions and Events:
1. CD Theory excels in representing complex actions and events. It allows for the
decomposition of actions into meaningful primitives and relationships, making it
suitable for modeling real-world scenarios.
2.Capture of Meaning and Context:
1. The theory is designed to capture the meaning of statements by representing
relationships between concepts. This makes it effective in maintaining context and
understanding the semantics of different situations.
3.Inference and Understanding:
1. CD Theory facilitates inference by allowing the system to reason about relationships
and dependencies. It supports understanding by providing a structured representation
that reflects the underlying meaning of the knowledge.

1.Contextual Information:
1. The use of case structures enables the inclusion of contextual information. Each
instance or case can have specific details filled in, allowing for a more nuanced
representation of knowledge.
2.Natural Language Processing:
1. CD Theory has been applied in natural language processing tasks. It provides a
framework for understanding and representing the meaning of sentences, making it
valuable for language understanding systems.
3.Learning and Adaptation:
1. CD Theory supports learning by allowing systems to adapt to new situations. The
representation of knowledge in terms of primitives and relationships enables flexibility in
updating and incorporating new information.
4.Problem Solving:
1. The structured representation of knowledge in CD Theory makes it suitable for problem-
solving tasks. It allows systems to analyze and reason about complex scenarios, making
it applicable in expert systems and artificial intelligence.

12)How inheritance is represented in Semantic Net? Explain the same with an
example
In Semantic Networks, inheritance is a mechanism that allows specific instances or subclasses
to inherit properties or attributes from more general classes or superclasses. It facilitates the
modeling of hierarchical relationships and the propagation of characteristics from higher-level
concepts to their more specialized counterparts. Inheritance helps in organizing and structuring
knowledge in a way that promotes reusability and simplifies the representation of complex
domains.
Example of Inheritance in Semantic Network:
Let's consider a simple example involving animals:
1.Superclass: Animal
1. Attributes:
1. Has Skin
2. Can Move
2.Subclass: Mammal (inherits from Animal)
1. Additional Attributes:
1. Gives Birth to Live Young
2. Produces Milk
3.Subclass: Bird (inherits from Animal)
1. Has Feathers
2. Lays Eggs

4.Subclass: Dog (inherits from Mammal)
1. Barks
2. Four Legs
In this Semantic Network:
•Animal is the superclass with attributes like "Has Skin" and "Can Move."
•Mammal and Bird are subclasses that inherit attributes from the Animal superclass. They
also have additional attributes specific to their subclasses.
•Dog is a subclass of Mammal, inheriting attributes from both Mammal and Animal. It also
has additional attributes specific to dogs.

Semantic Network Representation:
1.Animal
1. Attributes:
1. Has Skin
2. Can Move
2.Mammal (inherits from Animal)
1. Gives Birth to Live Young
2. Produces Milk
3.Bird (inherits from Animal)
1. Has Feathers
2. Lays Eggs
4.Dog (inherits from Mammal)
1. Barks
2. Four Legs
Querying the Network:
1.If you query for "Can Move":
1. All instances (Animal, Mammal, Bird, Dog) will respond positively
because this attribute is inherited.
2.If you query for "Has Feathers":
1. Only Bird will respond positively because it has this specific
attribute.

artificial intelligence that covers frames

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