Friday, December 10, 2010
Wednesday, December 8, 2010
This is true of ANNs as well. ANNs can process information at a great speed owing to their highly massive parallelism. Neural networks, with their remarkable ability to derive meaning from complicated or Imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. A trained neural network can be thought of as an "expert" in the category of information it has been given to analyse.
Biological Neural Networks
Much is still unknown about how the brain trains itself to process information, so theories abound. In the human brain, a typical neuron collects signals from others through a host of fine structures called dendrites . The neuron sends out spikes of electrical activity through a long, thin stand known as an axon , which splits into thousands of branches. At the end of each branch, a structure called a synapse converts the activity from the axon into electrical effects that inhibit or excite activity from the axon into electrical effects that inhibit or excite activity in the connected neurones. When a neuron receives Artificial Neural Networks Artificial neural networks are represented by a set of nodes, often arranged in layers, and a set of weighted directed links connecting them. The nodes are equivalent to neurons, while the links denote synapses. The nodes are the information processing units and the links acts as communicating media. There are a wide variety of networks depending on the nature of information processing carried out at individual nodes, the topology of the links, and the algorithm for adaptation of link weights. Some of the popular among them include:
The nueron sends out spikes of electrical activity through a long, thin strand known as an axon, which splits into thousands of brahches. At the end of each branch, a structure called a synapse converts the activity from te axon into electrcal effects that inhibit the activity in the connected neurons. When a nueron receieves excitatory input that sufficiently large compared to its inhiitary input, it sends a spike of electrical activity down its axon. Learning occurs by changing the effectiveness of the synapses so that the influence of one neuron on another changes.
• task T
Tuesday, November 30, 2010
The process of fuzzy reasoning is incorporated into what is called a Fuzzy Inferencing System. It is comprised of three steps that process the system inputs to the appropriate system outputs. These steps are 1) Fuzzification, 2) Rule Evaluation, and 3) Defuzzification. The system is illustrated in the following figure.
is the first step in the fuzzy inferencing process. This involves a domain formation where crisp inputs are transformed into fuzzy inputs. Crisp inputs are exact inputs measured by sensors and passed into the control system for processing, such as temperature, pressure, rpm's, etc.. Each crisp input that is to be processed by the FIU has its own group of membership functions or sets to which they are transformed. This group of membership functions exists within a universe of discourse that holds all relevant values that the crisp input can possess. The following shows the structure of membership functions within a universe of discourse for a crisp input.
2 Degree of membership:
degree to which a crisp value is compatible to a membership function, value from 0 to 1, also known as truth value or fuzzy input.membership function, MF: defines a fuzzy set by mapping crisp values from its domain to the sets associated degree of membership.
3.crisp inputs: distinct or exact inputs to a certain system variable, usually measured
4.parameters external from the control system, e.g. 6 Volts.
5.label: descriptive name used to identify a membership function.
6.scope: or domain, the width of the membership function, the range of concepts, usually numbers, over which a membership function is mapped.
7.universe of discourse: range of all possible values, or concepts, applicable to a system variable. When designing the number of membership functions for an input variable, labels must initially be determined for the membership functions. The number of labels correspond to the number of regions that the universe should be divided, such that each label describes a region of behavior. A scope must be assigned to each membership function that numerically identifies the range of input values that correspond to a label. The shape of the membership function should be representative of the variable. However this shape is also restricted by the computing resources available. Complicated shapes require more complex descriptive equations or large lookup tables. The next figure shows examples of possible shapes for membership functions.
language to describe states and operators. It is an efficient way to represent
Monday, November 29, 2010
Situation calculus is a version of first-order-logic (FOL) that is augmented so that it can reason about actions in time.
• Add situation variables to specify time. A situation is a snapshot of the world at
an interval of time when nothing changes
• Add a special predicate holds(f,s) that means "f is true in situation s"
• Add a function result(a,s) that maps the current situations into a new situation as
a result of performing action a.
Saturday, November 27, 2010
language to describe states and operators. It is an efficient way to represent planning
Representation of States and Goals
States are represented by conjunctions of function-free ground literals, that is, predicates
applied to constant symbols, possibly negated.
An example of an initial state is:
A state description does not have to be complete. We just want to obtain a successful plan
to a set of possible complete states. But if it does not mention a given positive literal, then
the literal can be assumed to be false.
Goals are a conjunction of literals. Therefore the goal is
1. Generate a goal to achieve
2. Construct a plan to achieve goal from current state
3. Execute plan until finished
4. Begin again with new goal
Friday, November 26, 2010
A simple planning agent is very similar to problem-solving agents in that it constructs
plans that achieve its goals, and then executes them. The limitations of the problem-
solving approach motivates the design of planning systems.
To solve a planning problem using a state-space search approach we would let the:
• initial state = initial situation
• goal-test predicate = goal state description
• successor function computed from the set of operators
• once a goal is found, solution plan is the sequence of operators in the path from
the start node to the goal node In searches, operators are used simply to generate successor states and we
can not look "inside" an operator to see how it’s defined. The goal-test predicate also is used as a
"black box" to test if a state is a goal or not. The search cannot use properties of how a
goal is defined in order to reason about finding path to that goal.
Hence this approach is all algorithm and representation weak.
Planning is considered different from problem solving because of the difference in the
way they represent states, goals, actions, and the differences in the way they construct
action sequences. Remember the search-based problem solver had four basic elements:
• Representations of actions: programs that develop successor state descriptions which
• Representation of state: every state description is complete. This is because a
complete description of the initial state is given, and actions are represented by a program
that creates complete state descriptions.
• Representation of goals: a problem solving agent has only information about it's
goal, which is in terms of a goal test and the heuristic function.
• Representation of plans: in problem solving, the solution is a sequence of actions.
In a simple problem:
"Get a quart of milk and a bunch of bananas and a variable speed
cordless drill" for a problem solving exercise we need to specify:
Initial State: the agent is at home without any objects that he is wanting.
Operator Set: everything the agent can do.
Heuristic function: the # of things that have not yet been acquired.
Problems with Problem solving agent: It is evident from the above figure that the actual branching factor
would be in the thousands or millions. The heuristic evaluation function can only choose states to
determine which one is closer to the goal. It cannot eliminate actions from
consideration. The agent makes guesses by considering actions and the evaluation
function ranks those guesses. The agent picks the best guess, but then has no idea
what to try next and therefore starts guessing again. It considers sequences of actions beginning from the
initial state. The agent is
forced to decide what to do in the initial state first, where possible choices are to
go to any of the next places. Until the agent decides how to acquire the objects, it
can't decide where to go.Planning emphasizes what is in operator and goal representations. There are three
key ideas behind planning:to "open up" the representations
of state, goals, and operators so that a reasoner
can more intelligently select actions when they are needed
the planner is free to add actions to the plan
wherever they are needed, rather
than in an incremental sequence starting at the initial state
most parts of the world are independent of most other parts
which makes it feasible to take a conjunctive goal and solve it with a divide-and-conquer strategy
2. Construct a plan to achieve goal from current state
3. Execute plan until finished
4. Begin again with new goal
The agent first generates a goal to achieve, and then constructs a plan to achieve it from
the current state. Once it has a plan, it keeps executing it until the plan is finished, then
begins again with a new goal.
A simple planning agent create and use plans based on the following assumptions:
Atomic time :each action is indivisible
No concurrent actions : allowed
Deterministic actions : result of each actions is completely determined by the
definition of the action, and there is no uncertainty in performing it in the world.
Agent is the sole cause of change: in the world.
Agent is omniscient : has complete knowledge of the state of the world
The student should understand the difference between problem solving and planning
and the need for knowledge representation in large scale problem solving
Students should understand the STRIPS planning language
Students should be able to represent a real life planning problem using STRIPS
Students should understand planning using situation calculus and the related frame
Students should understand the formulation of planning as a search problem
Students should learn the following planning algorithms
Situation space planning
Plan space planning
The student should understand the difficulties of full commitment planning
Students should understand the necessity of least commitment
Students should learn partial order planning algorithms
At the end of this lesson the student should be able to do the following:
Represent a planning problem in STRIPS language
Use a suitable planning algorithm to solve the problem.
Friday, September 24, 2010
What is the difference between Intelligence and Artificial Intelligence.?
The Birth and Development of Present day AI Research.
Important Events in the History of Artificial Intelligence Research.
What are the characterstics of Intelligence.?
What is AI Technique.?
What are the Knowledge Characterstics.?
What do you know about Production System,.?
Explain the concept of Control Stratagy.?
How can you analyze a Problem.?
Explain the concept of Operators or Production Rules.?
Define the problem as State Space Search Examine in detail.?
Define the problem as Satate Space Problem Sate and Prove Water Jug Proble,.?
Define the concept of Water Jug Problem. find three solutions to Water Jug Problem. Compare if there is a Water scarcity which solution is better and if there is no scarcity of Water which solution is better.?
Define the problem as State Space Problem state and prove Missionaries and Cannibals problem.?
By using State Space Problem write one solution to Tower of Honai Problem,.?
Explain the concept of AI problem characterstics in detail.?
What do you know about Heuristic Search.?
Explain the concept of Heuristic Search Techniques.?
Differentiate Problem Trees from Problem Graphs.?
Explain the concept of Depth First Search in detail.?
Explain the concept of Bredth First Search in detail.?
Explain the concept of Generate and Test Procedure.?
Why Heuristic Search Techniques are called WeekMethods.? Explain the same by using Nearest Neighbour Algorithem of Travelling Sales Man Problem.?
Explain the concept of Best First Search Procedure with an implementation of A* Algorithem.?
What do you know about OR arcs in A* algorithem.? explain in detail.?
Explain the concept of Problem Reduction Method with an implementation of AO* Algorithem.?
Difference between A* Algorithm and AO* Algorthm.?
What do you know about Constraint Satisfaction Procedure.? Explain the same with Constraint inference Rules by using search trees.?
Explain the concept of Means Ends Aanalysis Procedure.?
What do U mean by Knowledge Representation.?
Explain the concept of Symbolic Representaion of Knowledge.?
Knowledge Representation by using Pridicate Logic.
Knowledge Representation by using Proposational Logic.
Explain the Concept of Resolution Procedure.?
Explain the Concept of Resolution Pridicate Logic.?
Explain the Concept of Resolution in Proposational Logic.?
State and Prove Unification Algorithm.?
Explain the concept of Structured Representaion of Kowledge.?
Explain the concept of Semantic Net in detail.?
Explain the concept of Conceptual Dependency Diagrams.?
Write a Short Note on Frame and Script.
What do you know about Non-Monotonic Reasoning.?
Explain the concept of Probabilistic Reasoning.?
Expain the concept of Case Bsed Reasoning.?
What do U Know about Fuzzy Logic.?
Explain the concept of Goal Stack Planning.?
Explain the concept of Blocks world Problem.?
What do you know about Planning System.?
Explain the concept of Natural Language Processing.?
Explain the concept of Syntatic Analysis and Semantic Analysis.?
Expain the concept of CommonSence Aunthologies.?
Explain the concept of Qualitative Physics.?
What do U mean Expert System.? Explain the Architecture of Expert System.?
Explain about Expert System Development Process.?
What are the limitations of Expert System.? Differentiate a CaseBasedExpertSystem from ModelBasedExpertSystem.?
b. Define simple and steapest Hill Climbing.
c. What is heuristic function.?
d. Write the fact form of "Marcus was Tryassosinated by the Ceaser".?
e. What is inheritable Knowledge.?
f. What are the partitioned semantic nets.?
g. Define conditional planning.
h. What is expert system shell.?
Answer all the Questions 8 * 2 = 16 Marks.
1. Syntatic Processing.
3. semantic Net
5. Natural Deduction.
6. Frame Problem
7. Means ends Analysis.
8. AI Technique.
9. Memory Organization and Case Based Reasoning.
2. Tower of Honai Problem.
3. a. State AO* Algorithem.?
b. Characterstics of Production System.?
4. Difference between Procedural Knowledge and Declerative Knowledge.
5. Discuss the steps involved in converting a Standard form to Clause form.
6. Construct a Semantic Network with the suitable examples.?
7. List out the Advantages and Disadvantages that occur in writing the Scripts to represent the Knowledge.?
8. What are the characterstics of an Ideal Expert System.?
1.a)Distinguish between forward reasoning and backward reasoning?
b)What is AI.
c)What are the issues in knowledge representation.
d)What is meant by unsupervised learning.
e)Explain the term monotonicity.
f)What is state space search?
g)Define Symbolic Reasoning.
h)What is a frame?
2.a)Write Best-first-search algorithm with suitable example.
b)Explain various steps in water jug problem .
c)Discuss and distinguish b/w DFS and BFS .
d)Write Algorithm with suitable example.
3.a)What is a script? Write Restaurant Script in detail.
b)What are semantic nets? Explain with relavent example.
c)Discuss different issues of representing the knowledge.
d)Describe the representation of instance and is a relation.
4.a)Explain the concepts of Symbolic Reasoning and non-monotonic Reasoning.
b)Write short notes on Expert Systems.
5.a)Write a set of logical predicates that will perform simple automobile diagnostics (ex. If the engine won’t turn over and the lights won’t come on, then the battery is bad) don’t try to be too elaborate, but cover the cases of bad battery out of gas, bad spark plugs and bad starter motor.
b)Discuss the Unification Algorithm with suitable example.
c)What is Syntactic Analysis? Explain with suitable example.
THE PEDANANDIPADU COLLEGE OF ARTS & SCIENCES, PEDANANDIPADU
NOVEMBER/DECEMBER - 2006
Paper V-ARTIFICIAL INTELLIGENCE
Time:Three Hours Maximum.Marks:80M
Answer Question No.1 compulsorily, (8x2=16)
Answer ONE question from each Unit.(4x16=64)
All questions carry equal marks.
1.Write short notes on the following:
a)What do you mean by state space search?
c)What are the issues in knowledge presentation?
d)What is hill climbing?
e)Define Backward reasoning.
f)Represent the fact described by the following sentence as a set of wff’s in predicate
“Marcus tried to assassinate Caesar”.
h) What is the difference between forward and backward reasoning?
2.a)Write AO* algorithm with suitable example.
b)How do we define a problem as a state space search? Explain.
c)What is depth-first search technique? Give the algorithm compare it with breacdth-first technique.
d)What is “means-ends” analysis? Illustrate with an example.
3.a)Explain the issues in knowledge representation.
b)Briefly discuss property inheritance algorithm.
c)Describe about representation of instance and ‘Isa’ relationship.
d)Briefly explain about computable functions and predicates.
4.a)Explain the concept of non-monotonic reasoning.
b)Explain the concept of Dependency-Directed backtracking approach.
c)What are Frames ? Explain with example.
d)Explain the procedure of goal stack planning with suitable example.
5.a)What is Syntactic Analysis? Explain with suitable example.
b)Describe Unification grammars.
c)Discuss about Case-Based Reasoning.
d) Write short notes on Expert Systems.
- a) What do you mean by state space search?
b)With a suitable example explain how state space approach con be used to solve AI
2a)With suitable examples explain about ignorable, recoverable and irrecoverable
classes of problems. (10)
b)Explain the operation of bill climbing procedure and state the problems it has. (10)
3.a)With a suitable example explain how frames can be used to represent knowledge. (10)
b)Explain about property inheritance algorithm and show how it can be used for
default reasoning (10)
4.a)Show by resolution refutation that the following formula is a tautology.
(P ® Q) ® [ (R Ú P) ® (R Ú Q)] (8)
b)Consider the following statements: (12)
i)Whoever can read is literate.
ii)Dolphins are not literate.
iii)Some dolphins are intelligent and using, resolution prove the statement:
“Some who are intelligent can’t read”
5.a)Define Horn clause. Show how a Horn clause can be converted to PROLOG rule. (8)
b)With suitable examples show how ’cut’ and ‘fail’ predicates in PROLOG can be
used to control search. (12)
6.Explain the difference between forward and backward reasoning.
7.Define monotonic and partially commutative characteristics of a production system.
8.Briefly explain about means-ends analysis approach of solving problems.
9.Explain why scripts are called strong slot and filler structures.
10.Briefly explain about non-monotonic reasoning.
11.Write a PROLOG program to search for an element in a given list.
13.Explain the following:
d)Dynamic programming principle.
1.Write short notes on the following:
g) AND – OR graphs.
2.a)How do we define a problem as a state space search? Explain.
b)Explain the water jug problem and desctibe the production rules for the water jug
c)What are the probem characteristics to be analyzed before trying for a solution?
d)Analyse the traveling salesmen problem with respect to the characteristics discussed
3.a)What are the approaches to knowledge representation? And what are the issues
involved in it?
b)Describe the method of resolution in predicate logic with an example.
c)Consider the following sentences:
John likes all kinds of food.
Apples are food.
Chicken is food.
Anything anyone eats and isn’t killed by is food.
Bull eats peaqnuts and is still alive.
Sue eats everything Bull eats.
i)Translate these sentences into formulas in predicate logic.
ii)Convert the formulas of part a into clause form.
iii)Prove that John likes peanuts using resolution.
iv)Use resolution to answer the question “What food does Sue eat”?
4a)Explain the concept of nonmonotonic reasoning.
b)What is a semantic net? Explain, with an example, how partitioned semantic nets are
c)What are frames? Give an example.
d) Explain how slots are used to describe objects and inheritance.
5.a)Explain the cojncept of syntactic processing.
b) What is semantic analysis ? Explain.
c)What is a rule based expert system? Explain with an example.
d)What are commonsense antologies?