The document discusses the history and development of artificial intelligence. It defines AI as making computers do things that people are better at, like extending capabilities to large data or making fewer mistakes. Early AI research focused on games, mathematics, and knowledge-based systems. Over time, the focus shifted to symbolic and subsymbolic approaches, as well as robotics, language processing, and machine learning. Knowledge representation and commonsense reasoning remain challenging areas of research.
Intelligent Agent PPT ON SLIDESHARE IN ARTIFICIAL INTELLIGENCEKhushboo Pal
n artificial intelligence, an intelligent agent (IA) is an autonomous entity which acts, directing its activity towards achieving goals (i.e. it is an agent), upon an environment using observation through sensors and consequent actuators (i.e. it is intelligent).An intelligent agent is a program that can make decisions or perform a service based on its environment, user input and experiences. These programs can be used to autonomously gather information on a regular, programmed schedule or when prompted by the user in real time. Intelligent agents may also be referred to as a bot, which is short for robot.Examples of intelligent agents
AI assistants, like Alexa and Siri, are examples of intelligent agents as they use sensors to perceive a request made by the user and the automatically collect data from the internet without the user's help. They can be used to gather information about its perceived environment such as weather and time.
Infogate is another example of an intelligent agent, which alerts users about news based on specified topics of interest.
Autonomous vehicles could also be considered intelligent agents as they use sensors, GPS and cameras to make reactive decisions based on the environment to maneuver through traffic.
Examples of intelligent agents
AI assistants, like Alexa and Siri, are examples of intelligent agents as they use sensors to perceive a request made by the user and the automatically collect data from the internet without the user's help. They can be used to gather information about its perceived environment such as weather and time.
Infogate is another example of an intelligent agent, which alerts users about news based on specified topics of interest.
Autonomous vehicles could also be considered intelligent agents as they use sensors, GPS and cameras to make reactive decisions based on the environment to maneuver through traffic.
Intelligent Agent PPT ON SLIDESHARE IN ARTIFICIAL INTELLIGENCEKhushboo Pal
n artificial intelligence, an intelligent agent (IA) is an autonomous entity which acts, directing its activity towards achieving goals (i.e. it is an agent), upon an environment using observation through sensors and consequent actuators (i.e. it is intelligent).An intelligent agent is a program that can make decisions or perform a service based on its environment, user input and experiences. These programs can be used to autonomously gather information on a regular, programmed schedule or when prompted by the user in real time. Intelligent agents may also be referred to as a bot, which is short for robot.Examples of intelligent agents
AI assistants, like Alexa and Siri, are examples of intelligent agents as they use sensors to perceive a request made by the user and the automatically collect data from the internet without the user's help. They can be used to gather information about its perceived environment such as weather and time.
Infogate is another example of an intelligent agent, which alerts users about news based on specified topics of interest.
Autonomous vehicles could also be considered intelligent agents as they use sensors, GPS and cameras to make reactive decisions based on the environment to maneuver through traffic.
Examples of intelligent agents
AI assistants, like Alexa and Siri, are examples of intelligent agents as they use sensors to perceive a request made by the user and the automatically collect data from the internet without the user's help. They can be used to gather information about its perceived environment such as weather and time.
Infogate is another example of an intelligent agent, which alerts users about news based on specified topics of interest.
Autonomous vehicles could also be considered intelligent agents as they use sensors, GPS and cameras to make reactive decisions based on the environment to maneuver through traffic.
Artificial Intelligence (Current state and future of A.I) by Mudasir KhushkMudsaraliKhushik
Artificial Intelligence(Current state and future of A.I) by Mudasir Khushik student of University Of Sindh Campus at Thatta.
This Presentation will be really helpful for those students who are researching on Artificial Intelligence, these slides will tell you all about Artificial Intelligence its current state as well as its future.
Introduction Artificial Intelligence a modern approach by Russel and Norvig 1Garry D. Lasaga
In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. - Wikipedia
This seminar was presented by Ashish Kumar
A randomized algorithm is defined as an algorithm that typically
uses the random bits as an auxiliary input to guide its behavior. It achievs
good performance in the "average case". Formally, the algorithm's
performance will be a random variable determined by the random bits,
with (hopefully) good expected value. The "worst case" is typically so
unlikely to occur that it can be ignored. First and foremost reason for
using randomized algorithms is simplicity.
Artificial Intelligence - It's meaning, uses, past and future.
Artificial intelligence is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by animals including humans
Types of environment, Fully observable vs partially observable, static vs dynamic, deterministic vs stochastic, episodic vs sequential, discrete vs continuous, single agent vs multiagent, single agent vs multiagent,
The AML group carries out both theoretical and experimental work on developing and applying new machine learning techniques for solving various application problems.
Use of Artificial Intelligence in Cyber Security - Avantika UniversityAvantika University
There are many uses of artificial intelligence in cyber security. Although artificial intelligence has so many advantages over human intelligence, it is dependent on humans. Due to the ever-increasing demand for engineers, there is a bright scope in the field of cyber security. Avantika University is one of the top engineering colleges in India.
To know more details, visit us at : https://www.avantikauniversity.edu.in/engineering-colleges/use-of-artificial-intelligence-in-cyber-security.php
Artificial Intelligence (Current state and future of A.I) by Mudasir KhushkMudsaraliKhushik
Artificial Intelligence(Current state and future of A.I) by Mudasir Khushik student of University Of Sindh Campus at Thatta.
This Presentation will be really helpful for those students who are researching on Artificial Intelligence, these slides will tell you all about Artificial Intelligence its current state as well as its future.
Introduction Artificial Intelligence a modern approach by Russel and Norvig 1Garry D. Lasaga
In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. - Wikipedia
This seminar was presented by Ashish Kumar
A randomized algorithm is defined as an algorithm that typically
uses the random bits as an auxiliary input to guide its behavior. It achievs
good performance in the "average case". Formally, the algorithm's
performance will be a random variable determined by the random bits,
with (hopefully) good expected value. The "worst case" is typically so
unlikely to occur that it can be ignored. First and foremost reason for
using randomized algorithms is simplicity.
Artificial Intelligence - It's meaning, uses, past and future.
Artificial intelligence is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by animals including humans
Types of environment, Fully observable vs partially observable, static vs dynamic, deterministic vs stochastic, episodic vs sequential, discrete vs continuous, single agent vs multiagent, single agent vs multiagent,
The AML group carries out both theoretical and experimental work on developing and applying new machine learning techniques for solving various application problems.
Use of Artificial Intelligence in Cyber Security - Avantika UniversityAvantika University
There are many uses of artificial intelligence in cyber security. Although artificial intelligence has so many advantages over human intelligence, it is dependent on humans. Due to the ever-increasing demand for engineers, there is a bright scope in the field of cyber security. Avantika University is one of the top engineering colleges in India.
To know more details, visit us at : https://www.avantikauniversity.edu.in/engineering-colleges/use-of-artificial-intelligence-in-cyber-security.php
History of AI, Current Trends, Prospective TrajectoriesGiovanni Sileno
Talk given at the 2nd Winter Academy on Artificial Intelligence and International Law of the Asser Institute. The birth of AI: Dartmouth workshop. The biggest AI waves: classic symbolic AI (reasoning, knowledge systems, problem-solving), machine learning (induction). Current problems: explainability, trustworthyness, impact and transformation on society and people, the rise of artificially dumber systems.
by Samantha Adams, Met Office.
Originally purely academic research fields, Machine Learning and AI are now definitely mainstream and frequently mentioned in the Tech media (and regular media too).
We’ve also got the explosion of Data Science which encompasses these fields and more. There’s a lot of interesting things going on and a lot of positive as well as negative hype. The terms ML and AI are often used interchangeably and techniques are also often described as being inspired by the brain.
In this talk I will explore the history and evolution of these fields, current progress and the challenges in making artificial brains
From the FreshTech 2017 conference by TechExeter
www.techexeter.uk
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.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
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.
2. Our Working Definition of AI
Artificial intelligence is the study of how to make
computers do things that people are better at or
would be better at if:
• they could extend what they do to a World Wide
Web-sized amount of data and
• not make mistakes.
3. Why AI?
"AI can have two purposes. One is to use the power of
computers to augment human thinking, just as we use
motors to augment human or horse power. Robotics
and expert systems are major branches of that. The
other is to use a computer's artificial intelligence to
understand how humans think. In a humanoid way. If
you test your programs not merely by what they can
accomplish, but how they accomplish it, they you're
really doing cognitive science; you're using AI to
understand the human mind."
- Herb Simon
4. The Dartmouth Conference and the
Name Artificial Intelligence
J. McCarthy, M. L. Minsky, N. Rochester, and C.E.
Shannon. August 31, 1955. "We propose that a 2
month, 10 man study of artificial intelligence be
carried out during the summer of 1956 at
Dartmouth College in Hanover, New Hampshire.
The study is to proceed on the basis of the
conjecture that every aspect of learning or any
other feature of intelligence can in principle be
so precisely described that a machine can be
made to simulate it."
5. Time Line – The Big Picture
50 60 70 80 90 00 10
1956 Dartmouth conference.
1981 Japanese Fifth Generation project launched as the
Expert Systems age blossoms in the US.
1988 AI revenues peak at $1 billion. AI Winter begins.
academic $ academic and routine
6. The Origins of AI Hype
1950 Turing predicted that in about fifty years "an average
interrogator will not have more than a 70 percent chance of
making the right identification after five minutes of
questioning".
1957 Newell and Simon predicted that "Within ten years a
computer will be the world's chess champion, unless the rules
bar it from competition."
7. Evolution of the Main Ideas
•Wings or not?
•Games, mathematics, and other knowledge-poor tasks
•The silver bullet?
•Knowledge-based systems
•Hand-coded knowledge vs. machine learning
•Low-level (sensory and motor) processing and the resurgence
of subsymbolic systems
•Robotics
•Natural language processing
8. Symbolic vs. Subsymbolic AI
Subsymbolic AI: Model
intelligence at a level similar to
the neuron. Let such things as
knowledge and planning emerge.
Symbolic AI: Model such
things as knowledge and
planning in data structures that
make sense to the
programmers that build them.
(blueberry (isa fruit)
(shape round)
(color purple)
(size .4 inch))
9. The Origins of Subsymbolic AI
1943 McCulloch and Pitts A Logical Calculus of the Ideas
Immanent in Nervous Activity
“Because of the “all-or-none” character of nervous
activity, neural events and the relations among them can
be treated by means of propositional logic”
12. Games
• 1950 Claude Shannon published a paper describing how
a computer could play chess.
• 1952-1962 Art Samuel built the first checkers program
• 1957 Newell and Simon predicted that a computer will
beat a human at chess within 10 years.
• 1967 MacHack was good enough to achieve a class-C
rating in tournament chess.
• 1994 Chinook became the world checkers champion
• 1997 Deep Blue beat Kasparpov
• 2007 Checkers is solved
• Summary
13. Games
• AI in Role Playing Games – now we need knowledge
14. Logic Theorist
• Debuted at the 1956 summer Dartmouth conference, although
it was hand-simulated then.
• Probably the first implemented A.I. program.
• LT did what mathematicians do: it proved theorems. It
proved, for example, most of the theorems in Chapter 2 of
Principia Mathematica [Whitehead and Russell 1910, 1912,
1913].
• LT began with the five axioms given in Principia
Mathematica. From there, it began to prove Principia’s
theorems.
15. Logic Theorist
• LT used three rules of inference:
• Substitution (which allows any expression to be
substituted, consistently, for any variable):
• From: A ∧ B → A, conclude: fuzzy ∧ cute → fuzzy
• Replacement (which allows any logical connective to be
replaced by its definition, and vice versa):
• From A → B, conclude ¬A ∨ B
• Detachment (which allows, if A and A → B are theorems,
to assert the new theorem B):
• From man and man → mortal, conclude: mortal
16. Logic Theorist
In about 12 minutes LT produced, for theorem 2.45:
¬(p ∨ q) → ¬p (Theorem 2.45, to be proved.)
1. A → (A ∨ B) (Theorem 2.2.)
2. p → (p ∨ q) (Subst. p for A, q for B in 1.)
3. (A → B) → (¬B → ¬A) (Theorem 2.16.)
4. (p → (p ∨ q)) → (¬(p ∨ q) → ¬p) (Subst. p for A, (p ∨ q) for B in 3.)
5. ¬(p ∨ q) → ¬p (Detach right side of 4, using 2.)
Q. E. D.
17. Logic Theorist
The inference rules that LT used are not complete.
The proofs it produced are trivial by modern standards.
For example, given the axioms and the theorems prior to it, LT
tried for 23 minutes but failed to prove theorem 2.31:
[p ∨ (q ∨ r)] → [(p ∨ q) ∨ r].
LT’s significance lies in the fact that it opened the door to the
development of more powerful systems.
18. Mathematics
1956 Logic Theorist (the first running AI program?)
1961 SAINT solved calculus problems at the college
freshman level
1967 Macsyma
Gradually theorem proving has become well enough
understood that it is usually no longer considered AI.
20. What About Things that People Do
Easily?
• Common sense reasoning
• Vision
• Moving around
• Language
21. What About Things People Do
Easily?
• If you have a problem, think of a past situation where you
solved a similar problem.
• If you take an action, anticipate what might happen next.
• If you fail at something, imagine how you might have done
things differently.
• If you observe an event, try to infer what prior event might
have caused it.
• If you see an object, wonder if anyone owns it.
• If someone does something, ask yourself what the person's
purpose was in doing that.
22. They Require Knowledge
•Why do we need it?
•How can we represent it and use it?
•How can we acquire it?
Find me stuff about dogs who save people’s lives.
23. Why?
•Why do we need it?
•How can we represent it and use it?
•How can we acquire it?
Find me stuff about dogs who save people’s lives.
Two beagles spot a fire.
Their barking alerts
neighbors, who call 911.
24. Even Children Know a Lot
A story described in Charniak (1972):
Jane was invited to Jack’s birthday party. She wondered if
he would like a kite. She went into her room and shook her
piggy bank. It made no sound.
25. We Divide Things into Concepts
• What’s a party?
• What’s a kite?
• What’s a piggy bank?
26. What is a Concept?
Let’s start with an easy one: chair
42. How Can We Teach Things to Computers?
A quote from John McCarthy:
In order for a program to be capable of learning something,
it must first be capable of being told it.
Do we believe this?
43. Some Things are Easy
If dogs are mammals and mammals are animals, are dogs
mammals?
44. Some Things Are Harder
If most Canadians have brown eyes, and most brown eyed people
have good eyesight, then do most Canadians have good eyesight?
45. Some Things Are Harder
If most Canadians have brown eyes, and most brown eyed people
have good eyesight, then do most Canadians have good eyesight?
Maybe not for at least two reasons:
It might be true that, while most brown eyed people have good
eyesight, that’s not true of Canadians.
Suppose that 70% of Canadians have brown eyes and 70% of
brown eyed people have good eyesight. Then assuming that
brown-eyed Canadians have the same probability as other brown-
eyed people of having good eyesight, only 49% of Canadians are
brown eyed people with good eyesight.
48. Further Complications from How
Language is Used
• After the strike, the president sent them away.
• After the strike, the umpire sent them away.
The word “strike” refers to two different concepts.
49. When Other Words in Context Aren’t
Enough
• I need a new bonnet.
• The senator moved to table the bill.
50. Compiling Common Sense
Knowledge
• CYC (http://www.cyc.com)
• UT (http://www.cs.utexas.edu/users/mfkb/RKF/tree/ )
• WordNet (http://www.cogsci.princeton.edu/~wn/)
51. Distributed Knowledge Acquisition
• Acquiring knowledge for use by people
• Oxford English Dictionary
(http://oed.com/about/contributors/ )
• Wikipedia
• Acquiring knowledge for use by programs
• ESP (http://www.espgame.org/)
• Open Mind (http://commons.media.mit.edu:3000/)
• CYC (http://www.cyc.com)
55. The British Museum Algorithm
A simple algorithm: Generate and test
When done systematically, it is basic depth-first search.
But suppose that each time we end a path, we start over at the
top and choose the next path randomly. If we try this long
enough, we may eventually hit a solution. We’ll call this
The British Museum Algorithm or
The Monkeys and Typewriters Algorithm
http://www.arn.org/docs2/news/monkeysandtypewriters051103.htm
56. A Version of Depth-First Search:
Branch and Bound
Consider the problem of planning a ski vacation.
Fly to A $600 Fly to B $800 Fly to C $2000
Stay D $200
(800)
Stay E $250
(850)
Total cost
(1200)
57. Problem Reduction
Goal: Acquire TV
Steal TV Earn Money Buy TV
Or another one: Theorem proving in which we reason backwards
from the theorem we’re trying to prove.
58. Hill Climbing
Problem: You have just arrived in Washington, D.C.
You’re in your car, trying to get downtown to the
Washington Monument.
60. Hill Climbing – Is Close Good Enough?
A
B
Is A good enough?
• Choose winning lottery numbers
61. Hill Climbing – Is Close Good Enough?
A
B
Is A good enough?
• Choose winning lottery numbers
• Get the cheapest travel itinerary
• Clean the house
62. Expert Systems
Expert knowledge in many domains can be captured as rules.
Dendral (1965 – 1975)
If: The spectrum for the molecule has two peaks at masses x1 and
x2 such that:
• x1 + x2 = molecular weight + 28,
• x1 -28 is a high peak,
• x2 – 28 is a high peak, and
• at least one of x1 or x2 is high,
Then: the molecule contains a ketone group.
64. Expert Systems
1975 Mycin attaches probability-like numbers to rules:
If: (1) the stain of the organism is gram-positive, and
(2) the morphology of the organism is coccus, and
(3) the growth conformation of the organism is clumps
Then: there is suggestive evidence (0.7) that the identity of
the organism is stphylococcus.
65. Expert Systems – Today: Medicine
One example domain, medicine, has expert systems whose
tasks include:
• arrhythmia recognition from electrocardiograms
• coronary heart disease risk group detection
• monitoring the prescription of restricted use antibiotics
• early melanoma diagnosis
• gene expression data analysis of human lymphoma
• breast cancer diagnosis
66. Expert Systems – Today: Build Your Own
eXpertise2Go: http://www.expertise2go.com/
AcquiredIntelligence: http://www.aiinc.ca/demos/
(whales, graduate school)
67. Taking the AP Chemistry Exam
QUESTION: Sodium azide is used in air bags to rapidly produce
gas to inflate the bag. The products of the decomposition reaction
are:(a) Na and water.(b) Ammonia and sodium metal.(c) N2 and
O2(d) Sodium and nitrogen gas.(e) Sodium oxide and nitrogen
gas.
(d) Sodium and nitrogen gas.
68. SYSTEM-GENERATED ANSWER:
# What are the products of the given decomposition reaction?
•By definition, oxidation-reduction reactions occur when electrons are
transferred from the atom that is oxidized to the atom that is reduced. We need to
look for changes in the oxidation states of the elements in the reaction.
•In the reactants, the oxidation state(s) of the element Na is/are (1). In the
product, the oxidation state(s) is/are (0).
Therefore, the reaction causes a change in oxidation state.
•Therefore, this is an oxidation reduction reaction.
•By definition, a Binary Ionic-Compound Decomposition Reaction occurs when
a binary ionic compound is heated.
•Therefore, this reaction is a Binary-Ionic Compound Decomposition reaction.
•In general, a Binary Ionic-Compound Decomposition Reaction converts a
binary ionic-compound into basic elements.
•In this reaction, NaN3 reacts to produce Na and N2.
# The products of the decomposition reaction are:
(d) Sodium and nitrogen gas.
The work of Bruce Porter et al here at UT
Editor's Notes
Herbert A. Simon and Allen Newell, "Heuristic Problem Solving: The Next Advance in Operations Research," Operations Research, January-February 1958, 1-10.
Neural net picture from http://hem.hj.se/~de96klda/NeuralNetworks.htm#2.1.2%20The%20Artificial%20Neuron
From Crevier, AI: The Tumultuous History of the Search for Artificial Intelligence, p. 92.
From Crevier, AI: The Tumultuous History of the Search for Artificial Intelligence, p. 92.
British vs. American meaning of table, but both occur in the parliamentary context.
Try friend in Wordnet
Try friend in Wordnet
Suppose the solution is at depth 4 at the right. Then we must explore almost all paths of length 5 before we find it.
Suppose all paths of length 10 are solutions?
Note how much memory this approach takes.
Notice how much less memory this takes.
Note that we could have generated the answer but not noticed it.
Is depth-first search optimal? (I.e., if it finds the solution, it’s the best one) no. Can miss a better one somewhere else in the tree.
What’s the worst problem? Can go on forever. Can implement a cut off.
British Museum- find something there by walking randomly.
Monkeys and typewriters: enough monkeys, given enough time at enough typewriters, would eventually generate the complete works of Shakespeare.
It’s no longer the case that when we find one goal node we can stop. We want to find the cheapest acceptable trip. So we find one for $1200. We keep looking. But consider what happens when we generate the Fly to C node. Since all costs are positive, we know this trip will be more expensive than the first one we found. We can prune that entire subtree.