Ai lecture 04(unit-02)
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This document discusses planning problems in artificial intelligence and the STRIPS planning technique. It begins by defining a planning problem as choosing a sequence of actions to transform the state of the world to satisfy goals. It then describes STRIPS as a foundational planning language, noting its use of actions with preconditions and effects. Finally, it provides an example of solving a block world puzzle using STRIPS rules and operations like stack, unstack, pickup, and putdown.
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cs344-lect11-resolution-robotic-knowledge-representation-29jan08.ppt
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et
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te
etetq
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teq
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This document outlines the course details for CS 321 Algorithm Analysis and Design. It includes information on logistics like office hours and TAs, graded work like assignments and exams, starred problems, discussion policy, and an overview of design techniques, complexity issues, and key tools covered in the course. The document also provides pre-requisites and motivates the importance of algorithms through quotes about using both theory and practice to improve each other.
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Subject name Object Oriented Analysis and DesignMultiple Choice (.pdf
Subject name: Object Oriented Analysis and Design
Multiple Choice (8 points)
1) A subsystem is a system that functions as a component of another ________.
A) system
B) organization
C) computer
D) application
Answer:
2) The differences between a system and a network is that while the elements within a system
cannot function the same way if they are taken out of the system, elements within a network are
more or less able to function independently. Therefore, workstations connected to the Internet
are members of a ________.
A) computer system
B) network
C) universal system
D) information system
Answer:
3) Which of the following is NOT a major component of any information system?
A) applications
B) information technology
C) people
D) the company
Answer:
4) Information technology is the know-how, methods, tools, and materials used to support
________.
A) information systems
B) desk tops
C) virtual memory
D) network systems
Answer:
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A) airlines
B) government
C) manufacturing
D) all of the above
Answer:
6) A \"student becomes a graduate\" describes the ________ of object \"student.\"
A) state
B) attribute
C) name
D) identity
Answer:
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A) Attribute
B) Identity
C) Operation
D) State
Answer:
8) An infant boy grows to be a 80-year-old man. The new state of the object is ________.
A) a grandfather
B) an old and rich man
C) an old and wise man
D) all or any of the above
Answer:
9) Class is a set of objects that share the same ________.
A) name
B) state
C) attributes and operations
D) all of the above
Answer:
10) \"The employee name is Richard Smith and he checks the inventory periodically.\" In this
sentence, \"Richard Smith\" is the ________ of attribute \"name\"
A) class
B) object
C) value
D) operation
Answer:
11) Transforming the \"what\" into \"how\" is the job of ________.
A) analysis
B) a feasibility study
C) domain analysis
D) design
Answer:
12) Discovering the meaning of requirements within the context is the job of ________.
A) domain analysis
B) a feasibility study
C) design
D) implementation
Answer:
13) To succeed, the ad hoc approach must rely overwhelmingly on ________.
A) the ingenuity of participants to improvise solutions for unforeseen problems
B) the ability of the participants to coordinate and communicate with each other
C) \"luck\", meaning that the right people hit the right targets under the right circumstances
D) all of the above
Answer:
14) The waterfall method views development activities as predefined stage(s) of software
development such as ________.
A) a feasibility study
B) system investigation
C) system analysis and design
D) all of the above
Answer:
15) Which of the following is NOT one of the shortcomings of the waterfall method?
A) training of staff
B) detachment from the profession
C) inflexibility
D) over-reliance on documentation
Answer:
16) ________ are deploye.
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The document discusses problem solving using computers. It explains that programming languages allow problems to be solved repeatedly by telling the computer the logic or algorithm to follow. An algorithm is a series of steps, like pseudocode or a flowchart, that represent the procedure for solving a problem. It then provides examples of algorithms for calculating the factorial of a number in different formats, including English steps, flowcharts, and pseudocode. Programming implements the algorithm by translating it line-by-line into source code.
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Definition and divide-and-conquer strategies
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Ai lecture 13(unit03)
1) The document discusses proof by resolution in first-order logic (FOL) with two examples.
2) The first example aims to prove that "Raja is angry" from the facts that "Rimi is hungry", "If Rimi is hungry she barks", and "If Rimi is barking then Raja is angry".
3) The second example aims to prove that "Someone is smiling" from the facts that "All people who are graduating are happy", "All happy people smile", and "Someone is graduating".
Ai lecture 13(unit03)
Knowledge Based Reasoning: Agents, Facets of Knowledge. Logic and Inferences: Formal Logic,
Propositional and First Order Logic, Resolution in Propositional and First Order Logic, Deductive
Retrieval, Backward Chaining, Second order Logic. Knowledge Representation: Conceptual
Dependency, Frames, Semantic nets.
Ai lecture 12(unit03)
Knowledge Based Reasoning: Agents, Facets of Knowledge. Logic and Inferences: Formal Logic,
Propositional and First Order Logic, Resolution in Propositional and First Order Logic, Deductive
Retrieval, Backward Chaining, Second order Logic. Knowledge Representation: Conceptual
Dependency, Frames, Semantic nets.
Ai lecture 12(unit03)
Knowledge Based Reasoning: Agents, Facets of Knowledge. Logic and Inferences: Formal Logic,
Propositional and First Order Logic, Resolution in Propositional and First Order Logic, Deductive
Retrieval, Backward Chaining, Second order Logic. Knowledge Representation: Conceptual
Dependency, Frames, Semantic nets.
Ai lecture 11(unit03)
Knowledge Based Reasoning: Agents, Facets of Knowledge. Logic and Inferences: Formal Logic,
Propositional and First Order Logic, Resolution in Propositional and First Order Logic, Deductive
Retrieval, Backward Chaining, Second order Logic. Knowledge Representation: Conceptual
Dependency, Frames, Semantic nets.
Ai lecture 11(unit03)
Knowledge Based Reasoning: Agents, Facets of Knowledge. Logic and Inferences: Formal Logic,
Propositional and First Order Logic, Resolution in Propositional and First Order Logic, Deductive
Retrieval, Backward Chaining, Second order Logic. Knowledge Representation: Conceptual
Dependency, Frames, Semantic nets.
Ai lecture 10(unit03)
Knowledge Based Reasoning: Agents, Facets of Knowledge. Logic and Inferences: Formal Logic,
Propositional and First Order Logic, Resolution in Propositional and First Order Logic, Deductive
Retrieval, Backward Chaining, Second order Logic. Knowledge Representation: Conceptual
Dependency, Frames, Semantic nets.
Ai lecture 10(unit03)
Knowledge Based Reasoning: Agents, Facets of Knowledge. Logic and Inferences: Formal Logic,
Propositional and First Order Logic, Resolution in Propositional and First Order Logic, Deductive
Retrieval, Backward Chaining, Second order Logic. Knowledge Representation: Conceptual
Dependency, Frames, Semantic nets.
Ai lecture 09(unit03)
Knowledge Based Reasoning: Agents, Facets of Knowledge. Logic and Inferences: Formal Logic,
Propositional and First Order Logic, Resolution in Propositional and First Order Logic, Deductive
Retrieval, Backward Chaining, Second order Logic. Knowledge Representation: Conceptual
Dependency, Frames, Semantic nets.
Ai lecture 07(unit03)
1) Logic and inferences are important aspects of artificial intelligence as they allow systems to think and act rationally by making decisions based on available information and drawing conclusions.
2) Inference is the process of generating conclusions from facts and evidence. Formal logic represents knowledge through logical sentences using propositional or first-order logic.
3) Propositional logic uses symbolic variables to represent propositions that can be either true or false. Compound propositions combine simpler propositions using logical connectives like "and" and "or". Truth tables define the values of logical connectives.
Ai lecture 05(unit03)
This document discusses the Wumpus World environment, which is part of a lecture on artificial intelligence and robotics. The lecture covers logic and reasoning as it relates to the Wumpus World, an environment used to teach concepts in AI planning and problem solving. The document does not provide any additional details about the specific content or concepts covered regarding the Wumpus World environment.
Ai lecture 05(unit03)
Knowledge Based Reasoning: Agents, Facets of Knowledge. Logic and Inferences: Formal Logic,
Propositional and First Order Logic, Resolution in Propositional and First Order Logic, Deductive
Retrieval, Backward Chaining, Second order Logic. Knowledge Representation: Conceptual
Dependency, Frames, Semantic nets.
Ai lecture 04(unit03)
Knowledge Based Reasoning: Agents, Facets of Knowledge. Logic and Inferences: Formal Logic,
Propositional and First Order Logic, Resolution in Propositional and First Order Logic, Deductive
Retrieval, Backward Chaining, Second order Logic. Knowledge Representation: Conceptual
Dependency, Frames, Semantic nets.
Ai lecture 04(unit03)
Knowledge Based Reasoning: Agents, Facets of Knowledge. Logic and Inferences: Formal Logic,
Propositional and First Order Logic, Resolution in Propositional and First Order Logic, Deductive
Retrieval, Backward Chaining, Second order Logic. Knowledge Representation: Conceptual
Dependency, Frames, Semantic nets.
Ai lecture 03(unit03)
Knowledge Based Reasoning: Agents, Facets of Knowledge. Logic and Inferences: Formal Logic,
Propositional and First Order Logic, Resolution in Propositional and First Order Logic, Deductive
Retrieval, Backward Chaining, Second order Logic. Knowledge Representation: Conceptual
Dependency, Frames, Semantic nets.
Ai lecture 03(unit03)
Knowledge Based Reasoning: Agents, Facets of Knowledge. Logic and Inferences: Formal Logic,
Propositional and First Order Logic, Resolution in Propositional and First Order Logic, Deductive
Retrieval, Backward Chaining, Second order Logic. Knowledge Representation: Conceptual
Dependency, Frames, Semantic nets.
Ai lecture 003(unit03)
Knowledge Based Reasoning: Agents, Facets of Knowledge. Logic and Inferences: Formal Logic,
Propositional and First Order Logic, Resolution in Propositional and First Order Logic, Deductive
Retrieval, Backward Chaining, Second order Logic. Knowledge Representation: Conceptual
Dependency, Frames, Semantic nets.
Ai lecture 003(unit03)
Knowledge Based Reasoning: Agents, Facets of Knowledge. Logic and Inferences: Formal Logic,
Propositional and First Order Logic, Resolution in Propositional and First Order Logic, Deductive
Retrieval, Backward Chaining, Second order Logic. Knowledge Representation: Conceptual
Dependency, Frames, Semantic nets.
Ai lecture 02(unit03)
Knowledge Based Reasoning: Agents, Facets of Knowledge. Logic and Inferences: Formal Logic,
Propositional and First Order Logic, Resolution in Propositional and First Order Logic, Deductive
Retrieval, Backward Chaining, Second order Logic. Knowledge Representation: Conceptual
Dependency, Frames, Semantic nets.
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Ai lecture 04(unit-02)
- 1. Topic To Be Covered: I. Planning problem II. strips III. Solution to block world problem Jagdamba Education Society's SND College of Engineering & Research Centre Department of Computer Engineering SUBJECT: Artificial Intelligence & Robotics Lecture No-02(Unit-04) Prof.Dhakane Vikas N
- 2. PLANNING PROBLEM What is Planning Problem In AI? The planning problem in Artificial Intelligence is about the decision making performed by intelligent creatures like robots, humans or computer programs when trying to achieve some goal. It involves choosing a sequence of actions that will transform the state of the world and step by step so that it satisfy the goals. Planning:->Set of Actions. Action:->Precondition:->For actions to performed there is need of some preconditions. :->Effect:-Effect is result of action performed.
- 3. PLANNING PROBLEM Planning Generally consist of Following Things: I. Choose the best rule II. Applying rule on the problem to get new state. III. Detect if solution is found. IV. Detect dead end so that new direction will get explored.
- 4. strips What is STRIPS AI? After planning problem it is required to solve that problem and to solve problem planning language or techniques are required. Languages should be expressive enough to explain a wide variety of problems & must allow efficient algorithm to operate on it. Planning languages are known as action language. STRIPS was such technique/Language founded by Richard fike & Nilsson in 1971 as an automated planner. STRIPS stands for stands for Stanford Research Institute Problem Solver. It's an automated planning technique for finding goals by executing a domain from the initial state
- 5. PLANNING PROBLEM What is STRIPS AI? Later on this name was given other formal language . STRIPS is foundation for most of the language in order to express automated planning in current use. ADL:(Action Description Language) is an advancement of STRIPS, proposed by Pednault in 1987
- 6. Example of block world puzzle What is Block World Puzzle Problem? Block:-square block of same size can be stack on one another. Flat surface(Table) Robotic arm Stack
- 7. Example of block world puzzle Rules to Provide Solution for Block World Puzzle Problem RULE PRECONDITION ACTION Pickup(X) on(X, table) clear(X) handempty holding(X) Putdown(Y) Holding(X) on(X, table) clear(X) handempty Stack(X,Y) holding(X) clear(Y) On(X,Y) clear(X) Unstack(X,Y) On(X,Y) clear(X) holding(X) clear(Y)
- 8. Example of block world puzzle Basic Operations: stack(X,Y): put block X on block Y unstack(X,Y): remove block X from block Y pickup(X): pickup block X putdown(X): put block X on the table
- 9. Example of block world puzzle Example SOLUTION: Note: Initially goal state on STACK always. I. Compare initial state with goal state if match then stop, ELSE step (2) II.POP goal state from stack and apply suitable rule to perform action III.If all preconditions of rule satisfied then perform action. IV. If any precondition of certain rule satisfied then just POP that from stack ,don’t perform any action.
- 10. Example of block world puzzle Example SOLUTION: ON(A,B) Action: stack(A,B) Precondition: holding(A) clear(B) Action: pickup(A) Precondition: on(A, table) clear(A) hand empty