Knowledge representation in AI describes how knowledge can be structured to enable automated reasoning. There are several techniques for knowledge representation, including logical, semantic network, frame-based, and rule-based representations. Each technique has advantages and disadvantages for representing different types of knowledge such as concepts, facts, procedures, and meta-knowledge. Choosing the right knowledge representation approach depends on the requirements, including accurately and efficiently representing, storing, inferring, and acquiring knowledge.
Knowledge representation In Artificial IntelligenceRamla Sheikh
facts, information, and skills acquired through experience or education; the theoretical or practical understanding of a subject.
Knowledge = information + rules
EXAMPLE
Doctors, managers.
Knowledge representation In Artificial IntelligenceRamla Sheikh
facts, information, and skills acquired through experience or education; the theoretical or practical understanding of a subject.
Knowledge = information + rules
EXAMPLE
Doctors, managers.
The concept of intelligent system has emerged in information technology as a type of system derived from successful applications of artificial intelligence. The goal of this presentation is to give a general description of an intelligent system, which integrates classical approaches and recent advances in artificial intelligence. The presentation describes an intelligent system
in a generic way, identifying its main properties and functional components.
What is Heuristics?
A heuristic is a technique that is used to solve a problem faster than the classic methods. These techniques are used to find the approximate solution of a problem when classical methods do not. Heuristics are said to be the problem-solving techniques that result in practical and quick solutions.
Heuristics are strategies that are derived from past experience with similar problems. Heuristics use practical methods and shortcuts used to produce the solutions that may or may not be optimal, but those solutions are sufficient in a given limited timeframe.
History
Psychologists Daniel Kahneman and Amos Tversky have developed the study of Heuristics in human decision-making in the 1970s and 1980s. However, this concept was first introduced by the Nobel Laureate Herbert A. Simon, whose primary object of research was problem-solving.
Why do we need heuristics?
Heuristics are used in situations in which there is the requirement of a short-term solution. On facing complex situations with limited resources and time, Heuristics can help the companies to make quick decisions by shortcuts and approximated calculations. Most of the heuristic methods involve mental shortcuts to make decisions on past experiences.
Heuristic techniques
The heuristic method might not always provide us the finest solution, but it is assured that it helps us find a good solution in a reasonable time.
Based on context, there can be different heuristic methods that correlate with the problem's scope. The most common heuristic methods are - trial and error, guesswork, the process of elimination, historical data analysis. These methods involve simply available information that is not particular to the problem but is most appropriate. They can include representative, affect, and availability heuristics.
We can perform the Heuristic techniques into two categories:
Direct Heuristic Search techniques in AI
It includes Blind Search, Uninformed Search, and Blind control strategy. These search techniques are not always possible as they require much memory and time. These techniques search the complete space for a solution and use the arbitrary ordering of operations.
The examples of Direct Heuristic search techniques include Breadth-First Search (BFS) and Depth First Search (DFS).
Weak Heuristic Search techniques in AI
It includes Informed Search, Heuristic Search, and Heuristic control strategy. These techniques are helpful when they are applied properly to the right types of tasks. They usually require domain-specific information.
The examples of Weak Heuristic search techniques include Best First Search (BFS) and A*.
Knowledge representation and Predicate logicAmey Kerkar
This presentation is specifically designed for the in depth coverage of predicate logic and the inference mechanism :resolution algorithm.
feel free to write to me at : amecop47@gmail.com
The concept of intelligent system has emerged in information technology as a type of system derived from successful applications of artificial intelligence. The goal of this presentation is to give a general description of an intelligent system, which integrates classical approaches and recent advances in artificial intelligence. The presentation describes an intelligent system
in a generic way, identifying its main properties and functional components.
What is Heuristics?
A heuristic is a technique that is used to solve a problem faster than the classic methods. These techniques are used to find the approximate solution of a problem when classical methods do not. Heuristics are said to be the problem-solving techniques that result in practical and quick solutions.
Heuristics are strategies that are derived from past experience with similar problems. Heuristics use practical methods and shortcuts used to produce the solutions that may or may not be optimal, but those solutions are sufficient in a given limited timeframe.
History
Psychologists Daniel Kahneman and Amos Tversky have developed the study of Heuristics in human decision-making in the 1970s and 1980s. However, this concept was first introduced by the Nobel Laureate Herbert A. Simon, whose primary object of research was problem-solving.
Why do we need heuristics?
Heuristics are used in situations in which there is the requirement of a short-term solution. On facing complex situations with limited resources and time, Heuristics can help the companies to make quick decisions by shortcuts and approximated calculations. Most of the heuristic methods involve mental shortcuts to make decisions on past experiences.
Heuristic techniques
The heuristic method might not always provide us the finest solution, but it is assured that it helps us find a good solution in a reasonable time.
Based on context, there can be different heuristic methods that correlate with the problem's scope. The most common heuristic methods are - trial and error, guesswork, the process of elimination, historical data analysis. These methods involve simply available information that is not particular to the problem but is most appropriate. They can include representative, affect, and availability heuristics.
We can perform the Heuristic techniques into two categories:
Direct Heuristic Search techniques in AI
It includes Blind Search, Uninformed Search, and Blind control strategy. These search techniques are not always possible as they require much memory and time. These techniques search the complete space for a solution and use the arbitrary ordering of operations.
The examples of Direct Heuristic search techniques include Breadth-First Search (BFS) and Depth First Search (DFS).
Weak Heuristic Search techniques in AI
It includes Informed Search, Heuristic Search, and Heuristic control strategy. These techniques are helpful when they are applied properly to the right types of tasks. They usually require domain-specific information.
The examples of Weak Heuristic search techniques include Best First Search (BFS) and A*.
Knowledge representation and Predicate logicAmey Kerkar
This presentation is specifically designed for the in depth coverage of predicate logic and the inference mechanism :resolution algorithm.
feel free to write to me at : amecop47@gmail.com
Deciphering AI - Unlocking the Black Box of AIML with State-of-the-Art Techno...Analytics India Magazine
Most organizations understand the predictive power and the potential gains from AIML, but AI and ML are still now a black box technology for them. While deep learning and neural networks can provide excellent inputs to businesses, leaders are challenged to use them because of the complete blind faith required to ‘trust’ AI. In this talk we will use the latest technological developments from researchers, the US defense department, and the industry to unbox the black box and provide businesses a clear understanding of the policy levers that they can pull, why, and by how much, to make effective decisions?
Machine Learning an Exploratory Tool: Key Conceptsachakracu
This was an Online Lecture Describing Key Concepts of Machine Learning Strategies inclusing Neural Networks
National Webinar On Education 4.0 “Ensuring Continuity in Learning and Innovation Through Digitization”
Organized By: Singhad Institute of Management, Pune in Association with Savitribai Phule Pune University
12th June 2020
This presentation covers agent technology for artificial intelligence. Topics covered are as follows: expert systems, overcoming expert systems limitations, agent, what is an agent, definition of an agent, agents versus expert systems, how is an agent different from other software, types of agents, deliberate versus reactive, interface versus information, mobile versus stationary, and why a mobile agent.
Artificial Intelligence lecture notes. AI summarized notes for introduction to machine learning, symbol based and constructionist learning, also deep learning organized here for reading and may be for self-learning, I think.
The first lecture of expert system with python course.
Enjoy!
you can find the second lecture here:
https://www.slideshare.net/ahmadhussein45/expert-system-with-python-2
Pattern Recognition is the branch of machine learning a computer science which deals with the regularities and patterns in the data that can further be used to classify and categorize the data with the help of Pattern Recognition System.
“The assignment of a physical object or event to one of several pre-specified categories”-- Duda & Hart
Pattern Recognition System is responsible for generating patterns and similarities among given problem/data space, that can further be used to generate solutions to complex problems effectively and efficiently.
Certain problems that can be solved by humans, can also be made to be solved by machine by using this process.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
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Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
2. Introduction
• Human beings are good at understanding,
reasoning and interpreting knowledge.
• And using this knowledge, they are able to
perform various actions in the real world. But
how do machines perform the same?
3. What is Knowledge Representation?
• Knowledge Representation in AI describes the
representation of knowledge.
• Basically, it is a study of how the beliefs,
intentions, and judgments of an intelligent
agent can be expressed suitably for automated
reasoning.
• One of the primary purposes of Knowledge
Representation includes modeling intelligent
behavior for an agent.
4. What is Knowledge Representation?
• Knowledge Representation and Reasoning (KR,
KRR) represents information from the real world
for a computer to understand and then utilize this
knowledge to solve complex real-life problems like
communicating with human beings in natural
language.
• Knowledge representation in AI is not just about
storing data in a database, it allows a machine to
learn from that knowledge and behave intelligently
like a human being.
5. What is Knowledge Representation?
• The different kinds of knowledge that need to
be represented in AI include:
– Objects
– Events
– Performance
– Facts
– Meta-Knowledge
– Knowledge-base
7. Types of Knowledge
• Declarative Knowledge – It includes concepts, facts, and
objects and expressed in a declarative sentence.
• Structural Knowledge – It is a basic problem-solving
knowledge that describes the relationship between
concepts and objects.
• Procedural Knowledge – This is responsible for knowing
how to do something and includes rules, strategies,
procedures, etc.
• Meta Knowledge – Meta Knowledge defines knowledge
about other types of Knowledge.
• Heuristic Knowledge – This represents some expert
knowledge in the field or subject.
8. Cycle of Knowledge Representation in AI
• Artificial Intelligent Systems usually consist of
various components to display their intelligent
behavior. Some of these components include:
– Perception
– Learning
– Knowledge Representation & Reasoning
– Planning
– Execution
10. Example
• The Perception component retrieves data or
information from the environment.
• with the help of this component, you can
retrieve data from the environment, find out
the source of noises and check if the AI was
damaged by anything.
• Also, it defines how to respond when any sense
has been detected.
11. Example
• Then, there is the Learning Component that
learns from the captured data by the perception
component.
• The goal is to build computers that can be
taught instead of programming them. Learning
focuses on the process of self-improvement.
• In order to learn new things, the system
requires knowledge acquisition, inference,
acquisition of heuristics, faster searches, etc.
12. Example
• The main component in the cycle is Knowledge
Representation and Reasoning which shows the human-
like intelligence in the machines.
• Knowledge representation is all about understanding
intelligence.
• Instead of trying to understand or build brains from the
bottom up, its goal is to understand and build intelligent
behavior from the top-down and focus on what an agent
needs to know in order to behave intelligently.
• Also, it defines how automated reasoning procedures
can make this knowledge available as needed.
13. Example
• The Planning and Execution components
depend on the analysis of knowledge
representation and reasoning.
• Here, planning includes giving an initial state,
finding their preconditions and effects, and a
sequence of actions to achieve a state in which
a particular goal holds.
• Now once the planning is completed, the final
stage is the execution of the entire process.
14. Relation between Knowledge & Intelligence
• In the real world, knowledge plays a vital role in
intelligence as well as creating artificial
intelligence.
• It demonstrates the intelligent behavior in AI
agents or systems.
• It is possible for an agent or system to act
accurately on some input only when it has the
knowledge or experience about the input.
16. Example
• In this example, there is one decision-maker whose
actions are justified by sensing the environment
and using knowledge.
• But, if we remove the knowledge part here, it will
not be able to display any intelligent behavior.
• Now that you know the relationship between
knowledge and intelligence, let’s move on to the
techniques of Knowledge Representation in AI.
18. Logical Representation
• Logical representation is a language with some
definite rules which deal with propositions and has
no ambiguity in representation.
• It represents a conclusion based on various
conditions and lays down some important
communication rules.
• Also, it consists of precisely defined syntax and
semantics which supports the sound inference.
• Each sentence can be translated into logics using
syntax and semantics.
20. Logical Representation
• Advantages:
– Logical representation helps to perform logical
reasoning.
– This representation is the basis for the
programming languages.
• Disadvantages:
– Logical representations have some restrictions
and are challenging to work with.
– This technique may not be very natural, and
inference may not be very efficient.
21. Semantic Network Representation
• Semantic networks work as an alternative of predicate
logic for knowledge representation. In Semantic
networks, you can represent your knowledge in the
form of graphical networks.
• This network consists of nodes representing objects
and arcs which describe the relationship between
those objects. Also, it categorizes the object in
different forms and links those objects.
• This representation consist of two types of relations:
– IS-A relation (Inheritance)
– Kind-of-relation
23. Semantic Network Representation
• Advantages:
– Semantic networks are a natural representation of
knowledge.
– Also, it conveys meaning in a transparent manner.
– These networks are simple and easy to understand.
• Disadvantages:
– Semantic networks take more computational time at
runtime.
– Also, these are inadequate as they do not have any
equivalent quantifiers.
– These networks are not intelligent and depend on the
creator of the system.
24. Frame Representation
• A frame is a record like structure that consists of
a collection of attributes and values to describe
an entity in the world.
• These are the AI data structure that divides
knowledge into substructures by representing
stereotypes situations.
• Basically, it consists of a collection of slots and
slot values of any type and size.
• Slots have names and values which are called
facets.
25. Frame Representation
• Advantages:
– It makes the programming easier by grouping the related data.
– Frame representation is easy to understand and visualize.
– It is very easy to add slots for new attributes and relations.
– Also, it is easy to include default data and search for missing
values.
• Disadvantages:
– In frame system inference, the mechanism cannot be easily
processed.
– The inference mechanism cannot be smoothly proceeded by
frame representation.
– It has a very generalized approach.
26. Production Rules
• In production rules, agent checks for the condition and if
the condition exists then production rule fires and
corresponding action is carried out.
• The condition part of the rule determines which rule may
be applied to a problem. Whereas, the action part carries
out the associated problem-solving steps. This complete
process is called a recognize-act cycle.
• The production rules system consists of three main parts:
– The set of production rules
– Working Memory
– The recognize-act-cycle
27. Production Rules
• Advantages:
– The production rules are expressed in natural
language.
– The production rules are highly modular and can be
easily removed or modified.
• Disadvantages:
– It does not exhibit any learning capabilities and does
not store the result of the problem for future uses.
– During the execution of the program, many rules may
be active. Thus, rule-based production systems are
inefficient.
28. Representation Requirements
• A good knowledge representation system must have
properties such as:
– Representational Accuracy: It should represent all kinds
of required knowledge.
– Inferential Adequacy: It should be able to manipulate the
representational structures to produce new knowledge
corresponding to the existing structure.
– Inferential Efficiency: The ability to direct the inferential
knowledge mechanism into the most productive
directions by storing appropriate guides.
– Acquisitional efficiency: The ability to acquire new
knowledge easily using automatic methods.
29. Approaches to Knowledge Representation in AI
• Simple Relational Knowledge
– It is the simplest way of storing facts which uses
the relational method. Here, all the facts about a
set of the object are set out systematically in
columns.
– Also, this approach of knowledge representation
is famous in database systems where the
relationship between different entities is
represented.
– Thus, there is little opportunity for inference.
31. Approaches to Knowledge Representation in AI
• Inheritable Knowledge
– In the inheritable knowledge approach, all data
must be stored into a hierarchy of classes and
should be arranged in a generalized form or a
hierarchal manner.
– Also, this approach contains inheritable
knowledge which shows a relation between
instance and class, and it is called instance
relation.
– In this approach, objects and values are
represented in Boxed nodes.
33. Approaches to Knowledge Representation in AI
• Inferential Knowledge
• The inferential knowledge approach represents
knowledge in the form of formal logic. Thus, it can be
used to derive more facts. Also, it guarantees
correctness.
• Example:
Statement 1: John is a cricketer.
Statement 2: All cricketers are athletes.
Then it can be represented as;
– Cricketer(John)
– ∀x = Cricketer (x) ———-> Athelete (x)s
34. Issues in Knowledge Representation
• The fundamental goal of knowledge Representation is
to facilitate inference (conclusions) from knowledge.
• The issues that arise while using KR techniques are
many. Some of these are explained below.
• Important Attributed:
– Any attribute of objects so basic that they occur in
almost every problem domain?
– There are two attributed “instance” and “isa”, that are
general significance. These attributes are important
because they support property inheritance.
35. Issues in Knowledge Representation
• Relationship among attributes:
– Any important relationship that exists among object
attributed?
– The attributes we use to describe objects are
themselves entities that we represent.
– The relationship between the attributes of an
object, independent of specific knowledge they
encode, may hold properties like:
• Inverse — This is about consistency check, while a
value is added to one attribute. The entities are
related to each other in many different ways.
36. Issues in Knowledge Representation
• Existence in an isa hierarchy —
– This is about generalization-specification, like,
classes of objects and specialized subsets of those
classes, there are attributes and specialization of
attributes.
– For example, the attribute height is a specialization
of general attribute physical-size which is, in turn, a
specialization of physical-attribute.
– These generalization-specialization relationships
are important for attributes because they support
inheritance.
37. Issues in Knowledge Representation
• Technique for reasoning about values —
– This is about reasoning values of attributes
not given explicitly.
– Several kinds of information are used in
reasoning, like, height: must be in a unit of
length, Age: of a person cannot be greater
than the age of person’s parents.
– The values are often specified when a
knowledge base is created.
38. Issues in Knowledge Representation
• Single valued attributes —
– This is about a specific attribute that is
guaranteed to take a unique value.
– For example, a baseball player can at time
have only a single height and be a member of
only one team.
– KR systems take different approaches to
provide support for single valued attributes.
39. Issues in Knowledge Representation
• Choosing Granularity:
– At what level of detail should the knowledge be
represented?
– Regardless of the KR formalism, it is necessary to know:
• At what level should the knowledge be represented
and what are the primitives?
• Should there be a small number or should there be a
large number of low-level primitives or High-level
facts.
• High-level facts may not be adequate for inference
while Low-level primitives may require a lot of storage.
40. Issues in Knowledge Representation
• Example of Granularity:
Suppose we are interested in following facts:
John spotted Sue.
This could be represented as
Spotted (agent(John),object (Sue))
• Such a representation would make it easy to answer questions such are:
Who spotted Sue?
• Suppose we want to know:
Did John see Sue?
• Given only one fact, we cannot discover that answer.
• We can add other facts, such as
Spotted(x, y) -> saw(x, y)
• We can now infer the answer to the question.
41. Issues in Knowledge Representation
• Set of objects:
• How should sets of objects be represented?
• There are certain properties of objects that are true as
member of a set but not as individual;
• Example: Consider the assertion made in the sentences:
“there are more sheep than people in Australia”, and
“English speakers can be found all over the world.”
• To describe these facts, the only way is to attach
assertion to the sets representing people, sheep, and
English.
42. Issues in Knowledge Representation
• The reason to represent sets of objects is: if a
property is true for all or most elements of a set,
then it is more efficient to associate it once with the
set rather than to associate it explicitly with every
elements of the set.
• This is done,
– in logical representation through the use of
universal quantifier, and
– in hierarchical structure where node represent
sets and inheritance propagate set level assertion
down to individual.
43. Issues in Knowledge Representation
• Finding Right structure:
– Given a large amount of knowledge stored in
a database, how can relevant parts are
accessed when they are needed?
– This is about access to right structure for
describing a particular situation.
– This requires, selecting an initial structure
and then revising the choice.
44. Issues in Knowledge Representation
• While doing so, it is necessary to solve following
problems:
– How to perform an initial selection of the most
appropriate structure.
– How to fill in appropriate details from the current
situations.
– How to find a better structure if the one chosen
initially turns out not to be appropriate.
– What to do if none of the available structures is
appropriate.
– When to create and remember a new structure.
45. tushar@tusharkute.com
Thank you
This presentation is created using LibreOffice Impress 7.0.1.2, can be used freely as per GNU General Public License
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