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Module-I
▲ What is artificial intelligence?
Artificial Intelligence is the branch of computer science concerned with making computers behave like humans.
Major AI textbooks define artificial intelligence as "the study and design of intelligent agents," where an intelligent agent is a system that perceives its environment and takes actions which maximize its chances
of success. John McCarthy , who coined the term in 1956, defines it as "the science and engineering of making intelligent machines, especially intelligent computer programs."
The definitions of AI according to some text books are categorized into four approaches and are summarized in the table below:
Systems that think like humans “The exciting new effort to make computers think … machines with minds, in the full and literal sense.” (Haugeland,1985)
Systems that think rationally “The study of mental faculties through the use of computer models.” (Charniak and McDermont,1985)
Systems that act like humans The art of creating machines that perform functions that require intelligence when performed by people.” (Kurzweil,1990)
Systems that act rationally “ Computational intelligence is the study of the design of intelligent agents.” (Poole et al.,1998)
The four approaches in more detail are as follows:
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a. Acting humanly: The Turing Test approach
- (^) Test proposed by Alan Turing in 1950
- The computer is asked questions by a human interrogator.
The computer passes the test if a human interrogator, after posing some written questions, cannot tell whether the written responses come from a person or not. Programming a computer to pass, the computer need to possess the following capabilities:
♦ Natural language processing to enable it to communicate successfully in English. ♦ Knowledge representation to store what it knows or hears
♦ Automated reasoning to use the stored information to answer questions and to draw new conclusions. ♦ Machine learning to adapt to new circumstances and to detect and extrapolate patterns. To pass the complete Turing Test, the computer will need ♦ Computer vision to perceive the objects, and
♦ Robotics to manipulate objects and move about.
b. Thinking humanly: The cognitive modeling approach
We need to get inside actual working of the human mind: ■ through introspection – trying to capture our own thoughts as they go by; ■ through psychological experiments
Allen Newell and Herbert Simon, who developed GPS , the “ General Problem
Solver ” tried to trace the reasoning steps to traces of human subjects solving the same problems.
The interdisciplinary field of cognitive science brings together computer models from AI and experimental techniques from psychology to try to construct precise and testable theories of the workings of the human mind
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Computational units Storage units
Cycle time Bandwidth Memory updates/sec
1 CPU,10 8 gates 10 10 bits RAM
10 11 bits’ disk 10 -9^ sec
10 10 bits/sec 10 9
10 11 neurons 10 11 neurons
10 14 synapses 10 -3^ sec
10 14 bits/sec 10 14 Table 1.1 A crude comparison of the raw computational resources available to computers (circa 2003) and brain. The computer’s numbers have increased by at least by a factor of 10 every few years. The brain’s numbers have not changed for the last 10,000 years.
Brains and digital computers perform quite different tasks and have different properties. Table 1.1 shows that there are 10000 times more neurons in the typical human brain than there are gates in the CPU of a typical high-end computer. Moore’s Law predicts that the CPU’s gate count will equal the brain’s neuron count around 2020.
Psychology (1879 – present)
The origin of scientific psychology is traced back to the wok if German physiologist Hermann von Helmholtz (1821-1894) and his student Wilhelm Wundt (1832 – 1920)
In 1879, Wundt opened the first laboratory of experimental psychology at the university of Leipzig.
In US, the development of computer modeling led to the creation of the field of cognitive science.
The field can be said to have started at the workshop in September 1956 at MIT.
Computer engineering (1940-present)
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For artificial intelligence to succeed, we need two things: intelligence and an artifact. The computer has been the artifact of choice.
A1 also owes a debt to the software side of computer science, which has supplied the operating systems, programming languages, and tools needed to write modern programs
Control theory and Cybernetics (1948-present)
Ktesibios of Alexandria (c. 250 B.Sc.) built the first self-controlling machine: a water clock with a regulator that kept the flow of water running through it at a constant, predictable pace.
Modern control theory, especially the branch known as stochastic optimal control, has as its goal the design of systems that maximize an objective function over time.
Linguistics (1957-present)
Modem linguistics and AI, then, were "born" at about the same time, and grew up together, intersecting in a hybrid field called computational linguistics or natural language processing.
▲ The History of Artificial Intelligence
The gestation of artificial intelligence (1943-1955)
There were a number of early examples of work that can be characterized as AI, but it was Alan Turing who first articulated a complete vision of A1 in his 1950 article "Computing Machinery and Intelligence." There in, he introduced the Turing test, machine learning, genetic algorithms, and reinforcement learning.
The birth of artificial intelligence (1956)
McCarthy convinced Minsky, Claude Shannon, and Nathaniel Rochester to help him bring together U.S. researchers interested in automata theory,
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Figure 1.1 The Tom Evan’s ANALOGY program could solve geometric analogy problems as shown.
A dose of reality (1966-1973)
From the beginning, AI researchers were not shy about making predictions of their coming successes. The following statement by Herbert Simon in 1957 is often quoted:
“It is not my aim to surprise or shock you-but the simplest way I can summarize is to say that there are now in the world machines that think, that learn and that create. Moreover, their ability to do these things is going to increase rapidly until-in a visible future-the range of problems they can handle will be coextensive with the range to which the human mind has been applied.
Knowledge-based systems: The key to power? (1969-1979)
Dendral was an influential pioneer project in artificial intelligence (AI) of the
1960s, and the computer software expert system that it produced. Its primary aim was to help organic chemists in identifying unknown organic molecules, by analyzing their mass spectra and using knowledge of chemistry. It was done at Stanford University by Edward Feigenbaum, Bruce Buchanan, Joshua Lederberg, and Carl Djerassi.
A1 becomes an industry (1980-present)
In 1981, the Japanese announced the "Fifth Generation" project, a 10-year plan to build intelligent computers running Prolog. Overall, the A1 industry boomed from a few million dollars in 1980 to billions of dollars in 1988.
The return of neural networks (1986-present)
Psychologists including David Rumelhart and Geoff Hinton continued the study of neural-net models of memory.
A1 becomes a science (1987-present)
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In recent years, approaches based on hidden Markov models (HMMs) have come to dominate the area.
Speech technology and the related field of handwritten character recognition are already making the transition to widespread industrial and consumer applications.
The Bayesian network formalism was invented to allow efficient representation of, and rigorous reasoning with, uncertain knowledge.
The emergence of intelligent agents (1995-present)
One of the most important environments for intelligent agents is the Internet.
▲ The state of art
What can A1 do today?
Autonomous planning and scheduling: A hundred million miles from Earth, NASA's Remote Agent program became the first on-board autonomous planning program to control the scheduling of operations for a spacecraft (Jonsson et al., 2000). Remote Agent generated plans from high- level goals specified from the ground, and it monitored the operation of the spacecraft as the plans were executed-detecting, diagnosing, and recovering from problems as they occurred.
Game playing: IBM's Deep Blue became the first computer program to defeat the world champion in a chess match when it bested Garry Kasparov by a score of 3.5 to 2.5 in an exhibition match (Goodman and Keene, 1997).
Autonomous control: The ALVINN computer vision system was trained to steer a car to keep it following a lane. It was placed in CMU's NAVLAB computer-controlled minivan and used to navigate across the United States- for 2850 miles it was in control of steering the vehicle 98% of the time.
Diagnosis: Medical diagnosis programs based on probabilistic analysis have been able to perform at the level of an expert physician in several areas of medicine.
Logistics Planning: During the Persian Gulf crisis of 1991, U.S. forces deployed a Dynamic Analysis and Replanning Tool, DART (Cross and
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Our aim is to design agents.
A rational agent is one that performs the actions that cause the agent to be most
successful.
We use the term performance measure for the criteria that determine how
successful and agent is. We will insist on an objective performance measure
imposed by some authority.
Example Consider the case of an agent that is supposed to vacuum a dirty floor. A
plausible performance measure would be amount of dirt cleaned in a certain period
of time. A more sophisticated measure would include the amount of electricity
consumed and amount of noise generated.
We need to be careful to distinguish between rationality and omniscience. If an
agent is omniscient, it knows the actual outcomes of its actions. Rationality is
concerned with expected success given what has been perceived. In other words,
we cannot blame an agent for not taking into account something it could not
perceive or for failing to take an action that it is not capable of taking.
What is rational at any given time depends on four things:
- The performance measure that defines degree of success
- Everything that the agent has perceived so far (the percept sequence )
- What the agent knows about the environment
- The actions the agent can perform
The ideal rational agent:
For each possible percept sequence, an ideal rational agent should do whatever
action is expected to maximize its performance measure, on the basis of the
evidence provided by the percept sequence and whatever built-in knowledge the
agent has.
Ideal mapping from percept sequences to actions
For an ideal agent, we can simply make a table of the action it should take in
response to each possible percept sequence. (For most agents, this would be an
infinite table.) This table is called a mapping from the percept sequences to
actions.
Specifying which action an agent ought to take in response to any given percept
sequence provides a design for an ideal agent.
It is, of course, possible to specify the mapping for an ideal agent without creating
a table for every possible percept sequence.
Example : The sqrt agent
The percept sequence for this agent is a sequence of keystrokes representing a
number and an action is to display a number on a screen. The ideal mapping when
a percept is an positive number x is to display a positive number z such that z 2 =x.
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This specification does not require the designer to actually construct a table of
square roots.
Algorithms exist that make it possible to encode the ideal sqrt agent very
compactly. It turns out that the same is true for much more general agents.
One more requirement for agents: Autonomy
If an agent's actions are based completely on built-in knowledge, such that it need
pay no attention to its percepts, then we say that the agent lacks autonomy.
An agent's behaviour can depend both on its built-in knowledge and its experience.
A system is autonomous if it's behaviour is determined by it's own experience.
It seems likely that the most successful agents will have some built-in knowledge
and will also have the ability to learn.
Structure of Intelligent Agents
Now we start talking about the insides of agents.
The job of AI is to design the agent program : a function that implements the agent
mapping from percepts to actions. We assume this program will run on some sort
of computing device call the architecture.
The architecture makes the percepts from the sensors available to the agent
program. runs the program and feeds the program's action choices to the effectors
as they are generated.
- agent=architecture + program
Before we design an agent program, we must have a pretty good idea of the
possible percepts and actions, what goals or performance measure the agent is
supposed to achieve, and what sort of environment it will operate in.
Example A robot designed to inspect parts as they go by on a conveyer belt can
make use of a number of simplifying assumptions: that the lighting will always be
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The full taxi driver task is extremely open-ended - there is no limit to the novel
situations that can arise.
We start with percepts, actions, and goals
The taxi driver will need to know where it is, what else is on the road and how fast
it is going. This information can be obtained from the percepts provided by one or
more controllable cameras, a speedometer and odometer. To control the vehicle
properly it should have an accelerometer. It will need to know the state of the
vehicle, so it will need the usual arrays of engine and electrical sensors. It might
also have instruments like a GPS to give exact position with respect to an
electronic map. It might also have infrared or sonar sensors. Finally, it will need
some way for the customer to communicate destination.
The actions will include control over the engine through the accelerator pedal and
control over steering and braking. Some way of talking to passengers and perhaps
some way to communicate with other vehicles.
Performance measures?
Getting to the correct destination, minimizing fuel consumption and wear and tear,
minimizing trip time and cost, minimizing traffic violations and disturbances of
other drivers, maximizing safety and passenger comfort. Some of these goals
conflict and so there will be trade-offs.
Operating environment?
City streets? highways? snow and other road hazards? driving on right or left?
The more controlled the environment, the easier the problem.
We will now consider four types of agent programs:
- Agents that keep track of the world
Simple reflex agents
Constructing a lookup table is out of the question. The visual input from a simple
camera comes in at the rate of 50 megabytes per second, so the lookup table for an
hour would be 260x60x50M. However, we can summarize certain portions of the table
by noting certain commonly occurring input/output associations. For example, if
the car in front brakes, then the driver should also brake.
In other words some processing is done on visual input to establish the condition,
"brake lights in front are on" and this triggers some established connection to the
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action "start braking". Such a connection is called a condition-action rule written
as
If condition then action
Agents that keep track of the world
Simple reflex agents only work if the correct action can be chosen based only on
the current percept.
Even for the simple braking rule above, we need some sort of internal description
of the world state. (To determine if the car in front is braking, we would probably
need to compare the current image with the previous to see if the brake light has
come on.)
Another example
From time to time the driver looks in the rear view mirror to check on the location
of nearby vehicles. When the driver is not looking in the mirror, vehicles in the
next lane are invisible. However, in order to decide on a lane change requires that
the driver know the location of vehicles in the next lane.
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To do so requires hitting the brakes. The goal-based agent is more flexible but
takes longer to decide what to do.
Utility-based agents
Goals alone are not enough to generate high-quality behaviour. For example, there
are many action sequences that will get the taxi to its destination, but some are
quicker, safer, more reliable, cheaper, etc.
Goals just provide a crude distinction between "happy" and "unhappy" states
whereas a more general performance measure should allow a comparison of
different world states. "Happiness" of an agent is called utility.
Utility can be represented as a function that maps states into real numbers. The
larger the number the higher the utility of the state.
A complete specification of the utility function allows rational decisions in two
kinds of cases where goals have trouble. First, when there are conflicting goals,
only some of which can be achieved (e.g., speed vs. safety), the utility function
specifies the appropriate trade-off. Second, when there are several goals that the
agent can aim for, none of which can be achieved with certainty, utility provides a
way in which the likelihood of success can be weighed up against the importance
of the goals.
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Environments
Now we look at how agents couple with the environment.
Properties of environments
- Accessible vs. inaccessible
If an agent's sensory apparatus gives it access to the complete state of the
environment, we say that the environment is accessible. When the
environment is accessible, less internal state is necessary.
- Deterministic vs. non-deterministic
If the next state is completely determined by the current state and the actions
selected by the agent, then the environment is deterministic. An agent need
not worry about uncertainty in an accessible deterministic environment. If
the environment is inaccessible, it may appear to be nondeterministic.
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Intelligent agents are supposed to act in such a way that the environment goes
through a sequence of states that maximizes the performance measure.
Unfortunately, this specification is difficult to translate into a successful agent
design. The task is simplified if the agent can adopt a goal and aim to satisfy it.
Example Suppose the agent is in Auckland and wishes to get to Wellington. There
are a number of factors to consider e.g. cost, speed and comfort of journey.
Goals such as this help to organize behaviour by limiting the objectives that the
agent is trying to achieve. Goal formulation , based on the current situation is the
first step in problem solving. In addition to formulating a goal, the agent may wish
to decide on some other factors that affect the desirability of different ways of
achieving the goal.
We will consider a goal to be a set of states - just those states in which the goal is
satisfied. Actions can be viewed as causing transitions between states.
How can the agent decide on what types of actions to consider?
Problem formulation is the process of deciding what actions and states to
consider. For now let us assume that the agent will consider actions at the level of
driving from one city to another.
The states will then correspond to being in particular towns along the way.
The agent has now adopted the goal of getting to Wellington, so unless it is already
there, it must transform the current state into the desired one. Suppose that there
are three roads leaving Auckland but that none of them lead directly to Wellington.
What should the agent do? If it does not know the geography it can do no better
than to pick one of the roads at random.
However, suppose the agent has a map of the area. The purpose of a map is to
provide the agent with information about the states it might get itself into and the
actions it can take. The agent can use the map to consider subsequent steps of a
hypothetical journey that will eventually reach its goal.
In general, an agent with several intermediate options of unknown value can
decide what to do by first examining different possible sequences of actions that
lead to states of known value and then choosing the best one.
This process is called search. A search algorithm takes a problem as input and
returns a solution in the form of an action sequence. Once a solution is found, the
actions it recommends can be carried out. This is called the execution phase.
Hence, we have a simple "formulate, search, execute" design for the agent.
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Formulating Problems
Formulating problems is an art. First, we look at the different amounts of
knowledge that an agent can have concerning its actions and the state that it is in.
This depends on how the agent is connected to its environment.
There are four essentially different types of problems.
- single state
- multiple state
- contingency
- exploration
Knowledge and problem types
Let us consider the vacuum world - we need to clean the world using a vacuum
cleaner. For the moment we will simplify it even further and suppose that the
world has just two locations. In this case there are eight possible world states.
There are three possible actions: left, right, and suck. The goal is to clean up all the
dirt, i.e., the goal is equivalent to the set of states {7,8}.
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