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ARTIFICIAL INTELLIGENCE
L T P M C
UNIT I Introduction and Problem Solving I 9
Artificial Intelligence: Definition-Turing Test-Relation with other Disciplines-History of AI
Applications- Agent: Intelligent Agent-Rational Agent - Nature of Environments-Structure of Agent.-Problem Solving Agent - Problems: Toy Problems and Real-world Problems-Uninformed Search Strategies: BFS, DFS, DLS, IDS, Bidirectional Search -Comparison of uninformed search
strategies.
UNIT II Problem Solving II: 9 Informed Search Strategies-Greedy best-first search-A search-Heuristic functions-Local search*
Algorithms and Optimization problems - Online Search Agent-Constraint Satisfaction Problems- Backtracking Search for CSP’s –Local Search for Constraint Satisfaction Problems-Structure of
Problems -Adversarial Search-Optimal Decision in Games-Alpha-Beta Pruning-Imperfect Real Time Decisions-Games that Include an Element of Chance.
UNIT III Knowledge Representation 9
First-Order Logic-Syntax and Semantics of First-Order-Logic-Using First-Order-Logic-Knowledge Engineering in First-Order-Logic.- Inference in First-Order-Logic- Inference rules-Unification and
Lifting-Forward Chaining-Backward Chaining-Resolution.
UNIT IV Learning 9
Learning from Observations- Forms of Learning-Learning Decision – Ensemble Learning - A Logical
Formulation of Learning-Knowledge in Learning-Explanation Based Learning-Learning using Relevance Information-Inductive Logic Programming.
UNIT V Applications 9
Communication – Communication as action -A formal grammar for a fragment of English – Syntactic Analysis – Augmented Grammars – Semantic Interpretation – Ambiguity and
Disambiguation – Discourse Understanding – Grammar Induction. Perception – Image Formation – Early Image Processing Operations – Extracting Three Dimensional
Information – Object Recognition – Using Vision for Manipulation and Navigation. Total:
TEXT BOOKS:
1. Stuart Russell, Peter Norvig, “Artificial Intelligence – A Modern Approach”, 3rd^ Edition, Pearson
Education / Prentice Hall of India 2010(yet to be published).
2. Nils J. Nilsson, “Artificial Intelligence: A new Synthesis”, Harcourt Asia Pvt. Ltd,2003.
REFERENCES:
1. Elaine Rich and Kevin Knight, “Artificial Intelligence”, 2nd Edition, Tata McGraw-
Hill, 2003.
2. Patrick Henry Winston, “Artificial Intelligence”, Pearson Education / PHI, 2004.
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 total Turing test, the computer will need,
Computer vision: to perceive objects Robotics: to manipulate objects and move about
Thinking and Acting Humanly
Acting humanly
"If it looks, walks, and quacks like a duck, then it is a duck”
The Turing Test
Interrogator communicates by typing at a terminal with TWO other agents. The human can say and ask whatever s/he likes, in natural language. If the human cannot decide which of the two agents is a human and which is a computer, then the computer has achieved AI
this is an OPERATIONAL definition of intelligence, i.e., one that gives an algorithm for testing objectively whether the definition is satisfied
Thinking humanly: cognitive modeling
Develop a precise theory of mind, through experimentation and introspection, then write a computer program that implements it
Example: GPS - General Problem Solver (Newell and Simon, 1961)
trying to model the human process of problem solving in general
Thinking Rationally- The laws of thought approach
Capture ``correct'' reasoning processes”
A loose definition of rational thinking: Irrefutable reasoning process
How do we do this
Develop a formal model of reasoning (formal logic) that “always” leads to the “right” answer
Implement this model
How do we know when we've got it right?
when we can prove that the results of the programmed reasoning are correct
soundness and completeness of first-order logic
Example:
Ram is a student of III year CSE. All students are good in III year CSE.
Ram is a good student.
Acting Rationally
Act so that desired goals are achieved
The rational agent approach (this is what we’ll focus on in this course)
Figure out how to make correct decisions, which sometimes means thinking rationally and other times means having rational reflexes
correct inference versus rationality
reasoning versus acting; limited rationality
RELATION WITH OTHER DISCIPLINES:
- Expert Systems
- Natural Language Processor
- Speech Recognition
- Robotics
- Computer Vision
- Intelligent Computer-Aided Instruction
- Data Mining
- Genetic Algorithms
- Philosophy Logic, methods of reasoning, mind as physical system foundations of learning, language, rationality
- Mathematics Formal representation and proof algorithms, computation, (un)decidability, (in)tractability, probability
- Economics utility, decision theory
- Neuroscience physical substrate for mental activity
- Psychology phenomena of perception and motor control, experimental techniques
- Computer building fast computers engineering
- Control theory design systems that maximize an objective function over time
Agent : entity in a program or environment capable of generating action.
An agent uses perception of the environment to make decisions about actions to take.
The perception capability is usually called a sensor.
The actions can depend on the most recent perception or on the entire history (percept sequence).
An agent is anything that can be viewed as perceiving its environment through sensors and acting upon the environment through actuators.
Ex: Robotic agent
Human agent
Agent
Sensors
ACTUATORS
E N V I R O N M E N T
PERCEPT
S
ACTION
Agents interact with environment through sensors and actuators.
Percept sequence action
[A, clean] right
[A, dirt] suck
[B, clean] left
[B, dirty] suck
[A, clean], [A, clean] right
[A, clean], [A, dirty] suck
Fig: practical tabulation of a simple agent function for the vacuum cleaner world
Agent Function
The agent function is a mathematical function that maps a sequence of perceptions into action.
The function is implemented as the agent program.
The part of the agent taking an action is called an actuator.
environment sensors agent function actuators environment
A (^) B
Autonomy : the capacity to compensate for partial or incorrect prior knowledge (usually by learning).
NATURE OF ENVIRONMENTS:
Task environment – the problem that the agent is a solution to.
Includes
Performance measure
Environment
Actuator
Sensors
Agent Type Performance Measures
Environment Actuators Sensors
Taxi Driver Safe, Fast, Legal, Comfort, Maximize Profits
Roads, other traffic, pedestrians, customers
Steering, accelerators, brake, signal, horn
Camera, sonar, GPS, Speedometer, keyboard, etc
Medical diagnosis system
Healthy patient, minimize costs, lawsuits
Patient, hospital, staff
Screen display (questions, tests, diagnoses, treatments, referrals)
Keyboard (entry of symptoms, findings, patient's answers)
Properties of Task Environment:
- Fully Observable (vs. Partly Observable)
- Agent sensors give complete state of the environment at each point in time
- Sensors detect all the aspect that is relevant to the choice of action.
- An environment might be partially observable because of noisy and inaccurate sensors or apart of the state are simply missing from the sensor data.
- Deterministic (vs. Stochastic)
- Next state of the environment is completely determined by the current state and the action executed by the agent
- Strategic environment (if the environment is deterministic except for the actions of other agent.)
- Episodic (vs. Sequential)
- Agent’s experience can be divided into episodes, each episode with what an agent perceive and what is the action
- Next episode does not depend on the previous episode
- Current decision will affect all future sates in sequential environment
- Static (vs. Dynamic)
- Environment doesn’t change as the agent is deliberating
- Semi dynamic
- Discrete (vs. Continuous)
- Depends the way time is handled in describing state, percept, actions
- Chess game : discrete
- Taxi driving : continuous
- Single Agent (vs. Multi Agent)
- Competitive, cooperative multi-agent environments
- Communication is a key issue in multi agent environments.
Partially Observable:
Ex: Automated taxi cannot see what other devices are thinking.
Stochastic:
Ex: taxi driving is clearly stochastic in this sense, because one can never predict the behavior of the traffic exactly.
Semi dynamic:
If the environment does not change for some time, then it changes due to agent’s performance is called semi dynamic environment.
Single Agent Vs multi agent:
An agent solving a cross word puzzle by itself is clearly in a single agent environment.
An agent playing chess is in a two agent environment.
Example of Task Environments and Their Classes
Table-driven agents : the function consists in a lookup table of actions to be taken for every possible state of the environment.
If the environment has n variables, each with t possible states, then the table size is tn.
Only works for a small number of possible states for the environment.
Simple reflex agents : deciding on the action to take based only on the current perception and not on the history of perceptions.
Based on the condition-action rule:
(if (condition) action)
Works if the environment is fully observable
Four types of agents:
- Simple reflex agent
- Model based reflex agent
- goal-based agent
- utility-based agent
Simple reflex agent
Definition:
SRA works only if the correct decision can be made on the basis of only the current percept that is only if the environment is fully observable.
Characteristics
- do not know what they want to achieve
- do not know what they are doing
Condition-action rule
Ex : medical diagnosis system.
Algorithm Explanation:
UPDATE-INPUT : This is responsible for creating the new internal stated description.
Goal-based agents:
The agent has a purpose and the action to be taken depends on the current state and on what it tries to accomplish (the goal).
In some cases the goal is easy to achieve. In others it involves planning , sifting through a search space for possible solutions, developing a strategy.
Characteristics
- Action depends on the goal. (consideration of future)
- e.g. path finding
- Fundamentally different from the condition-action rule.
- Search and Planning
- Solving “car-braking” problem?
- Yes, possible … but not likely natural.
- Appears less efficient.
Utility-based agents
If one state is preferred over the other, then it has higher utility for the agent
Utility-Function (state) = real number (degree of happiness)
The agent is aware of a utility function that estimates how close the current state is to the agent's goal.
- Characteristics
- to generate high-quality behavior
- Map the internal states to real numbers.
(e.g., game playing)
- Looking for higher utility value utility function
Agent Example
A file manager agent.
Sensors: commands like ls, du, pwd.
Actuators: commands like tar, gzip, cd, rm, cp, etc.
Purpose: compress and archive files that have not been used in a while.
Environment: fully observable (but partially observed), deterministic (strategic), episodic, dynamic, discrete.
Agent vs. Program
Size – an agent is usually smaller than a program.
Purpose – an agent has a specific purpose while programs are multi-functional.
Persistence – an agent's life span is not entirely dependent on a user launching and quitting it.
Autonomy – an agent doesn't need the user's input to function.
Problem Solving Agents
- Problem solving agent
- A kind of “goal based” agent
- Finds sequences of actions that lead to desirable states.
Formulate Goal, Formulate Problem
Search
Execute
PROBLEMS
Four components of problem definition
- Initial state – that the agent starts in
- Possible Actions
- Uses a Successor Function
- Returns < action , successor > pair
- State Space – the state space forms a graph in which the nodes are states and arcs between nodes are actions.
- Path
- Goal Test – which determine whether a given state is goal state
- Path cost – function that assigns a numeric cost to each path.
Problem formulation is the process of deciding what actions and states to consider, given a goal
Path:
A path in the state space is a sequence of states connected by a sequence of actions.
The sequence of steps done by intelligent agent to maximize the performance measure:
Goal Formulation : based on the current situation and the agent’s performance measure, it is the first step in problem solving.
Problem Formulation : it is the process of deciding what actions and states to consider, given a goal.
Search : the process of looking for different sequence.
Solution: A search algorithm takes a problem as input and returns a solution in the form of an action sequence.
Execution: Once a solution is found, the actions it recommends can be carried out called execution phase.
Solutions
