UNIT 1 SRMIST KTR_problem and agents.pdf

18CSC305J ARTIFICIAL INTELLIGENCE
Introduction
1

ML and AI to build and optimize systems and also provide AI technology with
new data inputs for interpretation.
Artificial Intelligence

• ML and AI to build and optimize systems and also provide AI
technology with new data inputs for interpretation.
• Machine learning is about extracting knowledge from data.
• It is a research field at the intersection of statistics, artificial
intelligence, and computer science and is also known as predictive
analytics or statistical learning.
• AI and ML has become ubiquitous in everyday life
-commercial applications, data-driven research
Artificial Intelligence

Main reasons for AI advances
• Computing power
(GPU and Cloud computing)
• Big data
- Internet and sensors
- Large datasets
• Deep learning algorithms
- Software
- Improved techniques
- Toolboxes

What is AI?
8
Stuart J. Russell, Peter Norwig , Artificial Intelligence –A Modern approach

Acting humanly: Turing Test
• Turing (1950) "Computing machinery and intelligence":
• "Can machines think?" à "Can machines behave intelligently?"
• Operational test for intelligent behavior: the Imitation Game
The computer would need to possess the following capabilities:
• natural language processing to enable it to communicate successfully in English (or some
other human language);
• knowledge representation to store information provided before or during the interrogation;
• 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 total Turing Test, the computer will need
• computer vision to perceive objects
• robotics to move them about.
9

Thinking humanly: cognitive modeling
Determining how humans think
• through introspection—trying to catch our own thoughts as they go by
• through psychological experiments
Express the theory as a computer program
• program's input/output and timing behavior matches human behavior
10

Thinking rationally: "laws of thought"
•
•
• Aristotle: what are correct arguments/thought processes?
• Several Greek schools developed various forms of logic: notation and rules of derivation for thoughts;
may or may not have proceeded to the idea of mechanization
Direct line through mathematics and philosophy to modern AI
Problems:
1. Not all intelligent behavior is mediated by logical deliberation
2. What is the purpose of thinking? What thoughts should I have?
11

Acting rationally: rational agent
• Rational behavior: doing the right thing
• The right thing: that which is expected to maximize goal achievement, given the available
information
• An agent is just something that perceives and acts
• Doesn't necessarily involve thinking – but thinking should be in the service of rational action
12

Rational agents
• An agent is an entity that perceives and acts
• Abstractly, an agent is a function from percept histories to actions:
[f: P* à A]
• For any given class of environments and tasks, we seek the agent (or class of agents) with the best
performance
• computational limitations make perfect rationality unachievable
à design best program for given machine resources
13

History of AI
• AI has roots in a number of scientific disciplines
– computer science and engineering (hardware and software)
– philosophy (rules of reasoning)
– mathematics (logic, algorithms, optimization)
– cognitive science and psychology (modeling high
human/animal thinking)
– neural science (model low level human/animal brain activity)
– linguistics
level
• The birth ofAI (1943 – 1956)
– McCulloch and Pitts (1943): simplified mathematical model of
neurons (resting/firing states) can realize all propositional logic
primitives (can compute all Turing computable functions)
– Alan Turing: Turing machine and Turing test (1950)
– Claude Shannon: information theory; possibility of chess playing
computers
– Boole, Aristotle, Euclid (logics, syllogisms)
14

• Early enthusiasm (1952 – 1969)
– 1956 Dartmouth conference
John McCarthy (Lisp);
Marvin Minsky (first neural network machine);
Alan Newell and Herbert Simon (GPS);
– Emphasis on intelligent general problem solving
GSP (means-ends analysis);
Lisp (AI programming language);
Resolution by John Robinson (basis for automatic theorem
proving);
heuristic search (A*, AO*, game tree search)
• Emphasis on knowledge (1966 – 1974)
– domain specific knowledge is the key to overcome existing
difficulties
– knowledge representation (KR) paradigms
– declarative vs. procedural representation
History of AI
15

• Knowledge-based systems (1969 – 1999)
– DENDRAL: the first knowledge intensive system (determining 3D structures
of complex chemical compounds)
– MYCIN: first rule-based expert system (containing 450 rules for diagnosing
blood infectious diseases)
EMYCIN: an ES shell
– PROSPECTOR: first knowledge-based system that made significant profit
(geological ES for mineral deposits)
• AI became an industry (1980 – 1989)
– wide applications in various domains
– commercially available tools
– AI winter
• Current trends (1990 – present)
– more realistic goals
– more practical (application oriented)
– distributedAI and intelligent software agents
– resurgence of natural computation - neural networks and emergence of
genetic algorithms – many applications
– dominance of machine learning (big apps)
History of AI
16

State of the art
• HITECH, becomes the first computer program to defeat a grandmaster in a game
of chess (Arnold Denker)
• A speech understanding program named PEGASUS results in a confirmed
reservation that saves the traveller $894 over the regular coach fare.
• MARVEL, a real-time expert system that monitors the massive stream of data
transmitted by the spacecraft, handling routine tasks and alerting the analysts to
more serious problems.
17

• more powerful and more useful computers
• new and improved interfaces
• solving new problems
• better handling of information
• relieves information overload
• conversion of information into knowledge
Advantages of Artificial Intelligence
18

The Disadvantages
• increased costs
• difficulty with software development - slow and expensive
• few experienced programmers
• few practical products have reached the market as yet.
19

• AI deals with a large spectrum of Problems
• Applications spread across the domains, from medical to manufacturing with their own
complexities
• AI Deals with
• Various Day-to-day Problem
• Different identification and authentication problems (in security)
• Classification problems in Decision-making systems
• Interdependent and cross-domain problems (Such as Cyber-Physical
• Systems)
• The problems faced by AI is hard to resolve and also computationally
AI Technique
14
Parag Kulkarni, Prachi Joshi, Artificial Intelligence –Building Intelliegent Systems

AI Technique
• Intelligence requires knowledge
(less desirable properties)
– voluminous
– hard to characterize accurately
– constantly changing
– differ from data by being organized in a way that
corresponds to the ways it will be used
21

Knowledge Representation
• Generalizations-defines property
• Understood by people -eg taking readings
• Easily modified – correct errors and reflect changes
• Used in a great many situations(even not accurate or complete)
• Can be used to reduce the possibilities that must be considered(bulk
to narrow)
Categories of problems
• Structured problems –goal state defined
• Unstructured problems- goal state not known
• Linear problems- based on dependent variable
• Non linear problems- no dependency between variables
22

Problem
• In AI, formally define a problem as
• a space of all possible configurations where each configuration is called a state
• The state-space is the configuration of the possible states and how they connect
to each other e.g. the legal moves between states.
• an initial state
• one or more goal states
• a set of rules/operators which move the problem from one state to the next
• In some cases, we may enumerate all possible states
• but usually, such an enumeration will be overwhelmingly large so we only
generate a portion of the state space, the portion we are currently examining
• we need to search the state-space to find an optimal path from a start state to a
goal state.
17

State space: Tic-Tac-Toe
Goal: Arrange in horizontal or vertical or diagonal to win
24

State space: 8 Puzzle
The 8 puzzle search space consists of 8! states (40320)
25

Search
• Search is a general algorithm that helps in finding the path in state space
• The path may lead to the solution or dead end.
• Control strategies- overall rules and approach towards searching
i) forward search(data directed)
Starts search from initial state towards goal state.
Ex: locating a city from current location
ii) backward search(goal directed)
Search stars from goal state towards a solvable initial state.
Ex: start from target city
26

Search
• Strategies to explore the states
• Informed search – No guarantee for solution but high probability of getting solution
-heuristic approach is used to control the flow of solution path
-heuristic approach is a technique based on common sense, rule of
thumb, educated guesses or intuitive judgment
• Uninformed search – generates all possible states in the state space and checks for
the goal state.
- time consuming due to large state space
- used where error in the algorithm has severe consequences
• Parameters for search evaluation
i) completeness: Guaranteed to find a solution within finite time
ii) space and time complexity: memory required and time factor needed
iii) optimality and admissibility: correctness of the solution
27

Problem solving with AI
Well structured problems- yield right answer or inference for an algorithm
Ex:
• Quadratic equation-find value of x
• Speed of ball when reaches to batsman
• Network flow analysis
Ill structured problems-do not yield a particular answer
Ex:
• How to dispose wet waste safely
• Security threats in big social gathering
Unstructured problems- exact goal state not known(many goal states)
Ex: improve life expectancy of human being
Linear problems-have a solution or will not have
Non Linear problems-relationship between input and output are not linear
28

29

AI Applications
• Credit granting
• Information management and retrieval
• AI and expert systems embedded in products
• Plant layout
• Help desk and assistance
• Employee performance evaluation
• Shipping
• Marketing
• Warehouse optimization
• In space workstation maintainance
• Satellite controls
• Network developments
• Nuclear management
30

AI Models
Semiotic models- Based on sign process, signification or communication
Statistical models- representation and formalization of relationships through statistical techniques.
- History of data for decision making
- uses probabilistic approaches
31

Data Acquisition and Learning Aspects in AI
• Knowledge Discovery- data mining and machine learning:
data-recorded facts
information-pattern underlying the data
data mining or knowledge discovery-extraction of meaning information.
machine learning-algorithms that improve performance with experience
• Computational Learning Theory(COLT)- formal mathematical models defined
complexity-computation, prediction and feasibility
analyze patterns-Probably Approximately Correct(PAC)-hypothesis
mistake bound-target function
• Neural and evolutionary computation- speed up mining of data
evolutionary computing- biological properties
decision making and optimization
Neural computing-neural behavior of human being
pattern recognition and classification
32

• Intelligent agents and multi agent systems- decision making in complex scenarios
Intelligent agents –based on knowledge, available resources and
perspectives
multi agent systems- combination of more than one percept of intelligent
agents
• Multi-perspective integrated intelligence-utilizing and exploiting knowledge from
different perspective
Data Acquisition and Learning Aspects in AI
33

Problem Solving: Definition
A problem exists when you want to get from “here” (a knowledge state) to “there” (another
knowledge state) and the path is not immediately obvious.
Makes optimal use of knowledge and information to select set of actions for reaching the goal.
What are problems?
nEveryday experiences
nHow to get to the airport?
nHow to study for a quiz, complete a paper, and finish a lab before
recitation?
nDomain specific problems
nPhysics or math problems
nPuzzles/games
nCrossword, anagrams, chess
Categories of problem solving
• General purpose: means-end analysis
present situation is compared with the goal to detect the difference
select action that reduces the difference
Ex:select the mode of transport
• Special purpose-modelled for the specific problem, which have specific features
Ex: classify legal document reference to particular case
34

Problem solving process
• Problem solving-process of generating solutions for the given situation
• Problem is defined,
1. in a context
2. has well defined objective
3. solution has set of activities
4. uses previous knowledge and domain knowledge Primary
objective-problem identification
35

• Problem solving technique involves
1. problem definition
2. problem analysis and representation
3. planning
4. execution
5. evaluating solution
6. consolidating gains
• A search algorithm takes a problem as input and returns a solution in the form of
an action sequence.
• execution phase-Once a solution is found, the actions it recommends can be
carried out.
Problem solving process
36

• Problem formulation is the process of deciding what actions and
states to consider, and follows goal formulation.
• Goal formulation-the agent may wish to decide on some other factors
that affect the desirability of different ways of achieving the goal.
37
Formulating problems
Problem types:
Single-state problem-Agent knows exactly what each of its actions does and it can
calculate exactly which state it will be in after any sequence of actions.
Multiple-state problem-when the world is not fully accessible, the agent must
reason about sets of states that it might get to, rather than single states.
Contingency problem-the agent may be in need to now calculate a whole tree of
actions, rather than a single action sequence in which each branch of the tree deals
with a possible contingency that might arise.
Exploration problem-the agent learns a "map" of the environment, which it can then
use to solve subsequent problems.

• Well-defined problems and solutions
A problem is really a collection of information that the agent will use to decide what to do.
Elements of a problem:
1. The initial state that the agent knows itself to be in.
2. The set of possible actions available to the agent.
operator is used to denote the description of an action to
reach a state.
state space-the set of all states reachable from the initial state by
any sequence of actions.
A path in the state space is simply any sequence of actions
leading from one state to another.
3. The goal test, which the agent can apply to a single state description to determine if it is
a goal state.
4. A path cost function is a function that assigns a cost to a path.
The output of a search algorithm is a solution, that is, a path from the initial state to a state that
satisfies the goal test.
38

• Measuring problem-solving performance
Solution is obtained or not
Obtained solution is good solution or not(with a low path cost)
Search cost-associated with the time and memory required to find a solution.
total cost of the search is the sum of the path cost and the search cost
• Choosing states and actions
To decide a better solution, determine the measurement of path cost function
The process of removing detail from a representation is called abstraction
39

• Example: Water jug problem-find out a way to empty 2 galloon jug and fill 5 galloon jug with 1
galloon of water
states: Amount of water in the jugs
actions: 1. empty the big jug
2. empty the small jug
3. pour water from small jug to big jug
4. . pour water from big jug to small jug
Goal: 1 galloon of water in big jug and empty the small jug
path cost: number of actions(minimum number of actions->better solution)
Representation: jugs(b,s), where b-amount of water in bigger jug, s- b-amount of water in
smaller jug
initial state: (5,2)
goal state: (1,0)
operators: i) empty the jug
ii) fill the jug
Problem Formulation and Representation

• Solution:
initial state: (5,2)
goal state: (1,0)
operators:
i) empty big(remove water from big jug)
ii) empty small(remove water from small jug)
iii) big is empty(pour water from small jug to big jug)
iv) small is empty(pour water from big jug to small jug)
actions of sequence: 2,4,2,4,2
Problem Formulation and Representation
41

Toy problems
Intended to illustrate or exercise various problem-solving methods
The 8-puzzIe
• 3x3 board with eight numbered tiles and a blank space.
• A tile adjacent to the blank space can slide into the space.
• objective-to reach the configuration shown on the right of the figure.
Problem formulation:
• States: a state description specifies the location of each of the eight tiles in one of the nine
squares. For efficiency, it is useful to include the location of the blank.
• Operators: blank moves left, right, up, or down.
• Goal test: state matches the goal configuration shown in Figure.
• Path cost: each step costs 1, so the path cost is just the length of the path.
36

The 8-queens problem
• Place eight queens on a chessboard such that no queen attacks any other.
• There are two main kinds of formulation
The incremental formulation involves placing queens one by one
the complete-state formulation starts with all 8 queens on the board and moves
them around.
• Goal test: 8 queens on board, none attacked.
• Path cost: zero.
There are also different possible states and operators.
Consider the following for incremental formulation:
• States: any arrangement of 0 to 8 queens on board.
• Operators: add a queen to any square.
Consider the following for complete state formulation:
• States: arrangements of 8 queens, one in each column.
• Operators: move any attacked queen to another square in the same column.
Toy problems
43

The vacuum world
• Assume that the agent knows its location and the locations of all the pieces of dirt, and the
suction is still in good working order.
• States: one of the eight states
• Operators: move left, move right, suck.
• Goal test: no dirt left in any square.
• Path cost: each action costs 1.
Toy problems
44

• Letters stand for digits
• The aim is to find a substitution of digits for letters such that the resulting sum is arithmetically
correct.
• Each letter must stand for a different digit.
Problem formulation:
• States: a cryptarithmetic puzzle with some letters replaced by digits.
• Operators: replace all occurrences of a letter with a digit not already appearing in the puzzle.
• Goal test: puzzle contains only digits, and represents a correct sum.
• Path cost: zero. All solutions equally valid.
Toy problems
Cryptarithmetic
45

Route finding
• routing in
computer network
automated travel advisory systems
airline travel planning systems.
• the actions in the problem do not have completely known outcomes:
flights can be late or overbooked
connections can be missed
fog or emergency maintenance can cause delays.
• Other real world problems(refer Artificial Intelligence :A Modern Approach by Stuart J. Russell and
Peter Norvig page 69)
Touring and travelling salesman problem
VLSI layout
Robot navigation
Assembly sequencing
Real world problems
More difficult and whose solutions people actually care about
46

Problem types and Characteristics
Problem Characteristics:
To choose an appropriate method for a
particular Problem:
• Is the problem decomposable?
• Can solution steps be ignored or undone?
• Is the universe predictable?
• Is a good solution absolute or relative?
• Is the solution a state or a path?
• What is the role of knowledge?
• Does the task require human-interaction?
problem types
single-state problem-Agent knows exactly what
each of its actions does and it can calculate exactly which
state it will be in after any sequence of actions.
multiple-state problem-when the world is not
fully accessible, the agent must reason about sets of
states that it might get to, rather than single states.
contingency problem-the agent may be in need
to now calculate a whole tree of actions, rather than a
single action sequence in which each branch of the tree
deals with a possible contingency that might arise.
exploration problem-the agent learns a "map"
of the environment, which it can then use to solve
subsequent problems.
47
Kevin Night and Elaine Rich, Nair B., “Artificial Intelligence (SIE)”, Mc Graw Hill- 2008.

1.Is the problem decomposable?
• Can the problem be broken down to smaller
problems to be solved independently?
• Decomposable problem can be solved easily.
Ex 1:- ∫ x2 + 3x + sin2x cos 2x dx
This can be done by breaking it into three smaller problems and solving each
by applying specific rules. Adding the results the complete solution is
obtained.
Ex2: blocks world problem
Problem Characteristics
48

2. Can solution steps be ignored or undone?
1. Ignorable problems can be solved using a simple control structure that never
backtracks.
Ex:- theorem proving - In which solution steps can be ignored.(comment lines)
2. Recoverable problems can be solved using backtracking.
Ex:- 8 puzzle- In which solution steps can be undone(backtracking and rollback)
3. Irrecoverable problems can be solved by recoverable style methods via planning
Ex:- Chess- In which solution steps can’t be undone(Moves cannot be retracted.)
49

3.Is the universe predictable?
l Certain outcome-8-Puzzle
Every time we make a move, we know exactly what will
happen.
l Uncertain outcome-Playing Bridge
We cannot know exactly where all the cards are
or what the other players will do on their turns.
50

Eg: Marcus was a man.
Marcus was a Pompeian.
Marcus was born in 40 A.D.
All men are mortal.
All Pompeians died when the volcano erupted in 79 A.D.
No mortal lives longer than 150 years.
It is now 2004 A.D.
Is Marcus alive? (relative)
Different reasoning paths lead to the answer.
It does not matter which path we follow.
The Travelling Salesman Problem (absolute)
We have to try all paths to find the shortest one.
• Any-path problems can be solved using heuristics that suggest good paths to
explore.
• For best-path problems, much more exhaustive search will be performed.
4.Is a good solution absolute or relative?
51

5.Is the solution a state or a path?
Finding a consistent intepretation
“The bank president ate a dish of pasta salad with the fork”.
– “bank” refers to a financial situation or to a side of a river?
– “dish” or “pasta salad” was eaten?
– Does “pasta salad” contain pasta, as “dog food” does not
contain “dog”?
– Which part of the sentence does “with the fork” modify?
• A path-solution problem can be reformulated as a
state-solution problem by describing a state as a partial path to
a solution.
• The question is whether that is natural or not.
52

6.What is the role of knowledge?
l Playing Chess
Knowledge is important only to constrain the search for
a solution.
l Reading Newspaper
Knowledge is required even to be able to recognize a
solution.
53

7.Does the task require human-interaction?
l Solitary problem, in which there is no intermediate
communication and no demand for an explanation of
the reasoning process.
Eg: mathematical theorems
• Conversational problem, in which intermediate
communication is to provide either additional
assistance to the computer or additional information
to the user.
Eg: medical diagnosis
54

Agents
● An agent is anything that can be viewed as perceiving its environment
through sensors and acting upon that environment through actuators
• Human agent:
– eyes, ears, and other organs for sensors;
– hands, legs, mouth, and other body parts for actuators
• Robotic agent:
– cameras and infrared range finders for sensors
– various motors for actuators
56

● Intelligent Agent
● Rationality and Rational Agent
● Performance Measures
● Rationality and Performance
● Flexibility and Intelligent Agents
● Task environment and its properties
● Types of agents.

Rational Agent
• A rational agent is an agent which has clear preference, models
uncertainty, and acts in a way to maximize its performance measure
with all possible actions.
• A rational agent is said to perform the right things. AI is about creating
rational agents to use for game theory and decision theory for various
real-world scenarios.
• For an AI agent, the rational action is most important because in AI
reinforcement learning algorithm, for each best possible action, agent
gets the positive reward and for each wrong action, an agent gets a
negative reward.

Rational agents
• Rationality
– Performance measuring success
– Agents prior knowledge of environment
– Actions that agent can perform
– Agent’s percept sequence to date
• Ideal Rational Agent: For each possible percept sequence, an ideal rational agent
should select an action that is expected to maximize its performance measure,
given the evidence provided by the percept sequence and whatever built-in
knowledge the agent has.
61

SR
M
Rationality and Performance
►Rationality is with knowledge available with the agent
►Knowledge is derived from the percept sequence to date
►Learning agent which is part of rational agent that can succeed in variety
of environments
►Rational agent should possess the following properties
►Ability to gather information
►Ability to learn from the experience
►Perform knowledge augmentation
►Autonomy

Performance measure
•The criteria that determine how successful an agent is.
•One fixed measure is not suitable for all agents
• Performance measures: Safe, fast, legal, comfortable trip, maximize
profits
• Example:
• Agent: Interactive English tutor
• Performance measure: Maximize student's score on test
• Environment: Set of students
• Actuators: Screen display (exercises, suggestions, corrections)
• Sensors: Keyboard
63

Flexibility
• Autonomy means “Independence”
• A system without autonomy lacks flexibility
• Equips agent to negotiate with dynamic scenario, i.e., system
should be able to adapt with the changing scenario and should
exhibit rational behaviour in the changing scenario.
• A system is autonomous if its behavior is determined by its own
experience
• An alarm that goes off at a prespecified time is not autonomous
• An alarm that goes off when smoke is sensed is autonomous
64

Intelligent Agents
• An intelligent agent is an autonomous entity which act upon an environment
using sensors and actuators for achieving goals.
• An intelligent agent may learn from the environment to achieve their goals.
• Following are the main four rules for an AI agent:
● Rule 1: An AI agent must have the ability to perceive the environment.
● Rule 2: The observation must be used to make decisions.
● Rule 3: Decision should result in an action.
● Rule 4: The action taken by an AI agent must be a rational action.

SR
M
Intelligent Agent
►Agent - Entity that can perceive the information and act on that information to achieve the
desired outcome
►Intelligent Agent - Capable of making decisions and act most logically
►Agent Interacts with environment through sensors and actuators

Examples
1. Consider an automatic attendance system. In the system, fingerprint sensor
senses the fingerprints, actuator gives the message and the attendance is marked
for the employee corresponding to sensed fingerprint.
2. Auto door opening and closing system, a camera is used as sensor for sensing the
existence of an object. When the camera senses that a person is standing in front
of the door or near the door, it gives this input to the agent program (percepts).
Agent gives command and then the actuator opens the door.

PEAS Representation
PEAS is a type of model on which an AI agent works upon. When we
define an AI agent or rational agent, then we can group its properties
under PEAS representation model. It is made up of four words:
● P: Performance measure
● E: Environment
● A: Actuators
● S: Sensors
Here performance measure is the objective for the success of an agent's
behavior.

PEAS for self-driving cars
Let's suppose a self-driving car then PEAS representation will be:
Performance: Safety, time, legal drive, comfort
Environment: Roads, other vehicles, road signs, pedestrian
Actuators: Steering, accelerator, brake, signal, horn
Sensors: Camera, GPS, speedometer, odometer, accelerometer,
sonar.

Agents with PEAS representation
Agent Performance measure Environment Actuators Sensors
1.Medical
Diagnose
● Healthy patient
● Minimized cost
● Patient
● Hospital
● Staff
● Tests
● Treatments
Keyboard
(Entry of symptoms)
2.Vacuum Cleaner ● Cleanness
● Efficiency
● Battery life
● Security
● Room
● Table
● Wood floor
● Carpet
● Various obstacles
● Wheels
● Brushes
● Vacuum Extractor
● Camera
● Dirt detection sensor
● Cliff sensor
● Bump Sensor
● Infrared Wall Sensor
3. Part -picking
Robot
● Percentage of parts in
correct bins.
● Conveyor belt with
parts,
● Bins
● Jointed Arms
● Hand
● Camera
● Joint angle sensors.

SR
M
Types of Agents
►Types based on Complexity and functionality (Expected Intelligence) of
agent
►Complexity of the environment contributes contributes to complexity of the
agent architecture
►Types of Agents are,
►Table-driven agents
►Simple Reflex Agents
►Model-based reflex Agents
►Goal-based Agents
►Utility-based Agents

Simple Reflex Agent
• Simplest agent which acts according to the current percept only,
pays no attention to the rest of the percept history.
• The agent function of this type relies on the condition-action rule –
"If condition, then action."
• It makes correct decisions only if the environment is fully
observable.
• Example: iDraw, a drawing robot which converts the typed characters
into writing without storing the past data.

Model-based reflex agents
• These type of agents can handle partially observable environments by
maintaining some internal states. (Agents with memory)
• The internal state depends on the percept history, which reflects at least
some of the unobserved aspects of the current state.
• Therefore, as time passes, the internal state needs to be updated which
requires two types of knowledge or information to be encoded in an agent
program i.e., the evolution of the world on its own and the effects of the
agent's actions.

Model Based Reflex Agent
Example: When a person walks in a lane, he maps the pathway in his mind.

UNIT 1 SRMIST KTR_problem and agents.pdf

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UNIT 1 SRMIST KTR_problem and agents.pdf