This document provides an overview of an artificial intelligence course syllabus and key concepts in AI. It discusses topics like what AI is, the foundations and history of AI, production systems, state space search techniques including informed and uninformed searches. It also covers knowledge representation, reasoning, machine learning, computer vision, robotics and common AI problems. Key problems in AI like perception, natural language understanding, commonsense reasoning and more are explained.
The document discusses problem solving and the problem solving cycle. It describes the problem solving cycle as having 7 steps: problem identification, definition and representation, strategy formulation, organization of information, resource allocation, monitoring, and evaluation. It also discusses different types of problems, distinguishing between well-structured and ill-structured problems. Finally, it provides an example of solving the water jug problem using a state space representation and production rules to systematically search for a solution.
The document provides an introduction to state space search problems and algorithms. It discusses key concepts like the state space representation, initial and goal states, actions/operators that transform states, and different search strategies. Specific examples covered include the vacuums world problem, towers of Hanoi, water jugs problem, and the 8 queens puzzle. The document also introduces production systems and how they can be used to represent state space search problems.
AI-04 Production System - Search Problem.pptxPankaj Debbarma
Production Systems
A simple string rewriting production system example
Search Problem
Basic searching process
Algorithm’s performance and complexity
Computational complexity
‘Big - O’ notation
Tower of Hanoi
8 Puzzle
Water Jug Problem
Can Solution Steps be Ignored
Is Good Solution Absolute or Relative
Issues in the Design of Search Programs
The document provides an overview of artificial intelligence planning, including definitions and key concepts. It describes planning as a deliberative process that chooses and organizes actions to achieve goals. A conceptual model of planning problems uses state-transition systems and graphs to represent the problem. The document contrasts toy problems from real-world planning problems and discusses formulating planning as a search problem to find a solution path.
This document discusses state space search problems and algorithms. It begins by outlining the key components of goal-based agents: the goal, actions, and state representation. Several example problems are then described in more detail, including the 8-puzzle, missionaries and cannibals, cryptarithmetic, and water jug problems. For each problem, the document specifies the goal, state representation, initial state, and possible actions/operators. It also discusses issues in knowledge representation and choosing an appropriate level of abstraction for problem states.
This document discusses state space search algorithms. It defines key concepts like the state representation, operators/actions, initial and goal states. Example problems are presented like the 8-puzzle, missionaries and cannibals, cryptarithmetic etc. Generic state space search is formalized using a graph of nodes and operators. Key procedures like expand, goal test and queueing functions are discussed. Bookkeeping, search tree issues and ways to evaluate strategies are also covered at a high level.
The document provides information on various topics related to artificial intelligence including:
- Examples of intelligence such as solving puzzles, performing complex math problems quickly, and following rules.
- Definitions of AI from early researchers such as John McCarthy who coined the term, and descriptions of AI as the study of intelligent behavior in machines.
- Key areas of AI research and applications such as game playing, reasoning, learning, robotics, and machine learning.
- Approaches to problem solving in AI like state space search, knowledge representation, and using heuristics to guide searches.
The document discusses problem solving and the problem solving cycle. It describes the problem solving cycle as having 7 steps: problem identification, definition and representation, strategy formulation, organization of information, resource allocation, monitoring, and evaluation. It also discusses different types of problems, distinguishing between well-structured and ill-structured problems. Finally, it provides an example of solving the water jug problem using a state space representation and production rules to systematically search for a solution.
The document provides an introduction to state space search problems and algorithms. It discusses key concepts like the state space representation, initial and goal states, actions/operators that transform states, and different search strategies. Specific examples covered include the vacuums world problem, towers of Hanoi, water jugs problem, and the 8 queens puzzle. The document also introduces production systems and how they can be used to represent state space search problems.
AI-04 Production System - Search Problem.pptxPankaj Debbarma
Production Systems
A simple string rewriting production system example
Search Problem
Basic searching process
Algorithm’s performance and complexity
Computational complexity
‘Big - O’ notation
Tower of Hanoi
8 Puzzle
Water Jug Problem
Can Solution Steps be Ignored
Is Good Solution Absolute or Relative
Issues in the Design of Search Programs
The document provides an overview of artificial intelligence planning, including definitions and key concepts. It describes planning as a deliberative process that chooses and organizes actions to achieve goals. A conceptual model of planning problems uses state-transition systems and graphs to represent the problem. The document contrasts toy problems from real-world planning problems and discusses formulating planning as a search problem to find a solution path.
This document discusses state space search problems and algorithms. It begins by outlining the key components of goal-based agents: the goal, actions, and state representation. Several example problems are then described in more detail, including the 8-puzzle, missionaries and cannibals, cryptarithmetic, and water jug problems. For each problem, the document specifies the goal, state representation, initial state, and possible actions/operators. It also discusses issues in knowledge representation and choosing an appropriate level of abstraction for problem states.
This document discusses state space search algorithms. It defines key concepts like the state representation, operators/actions, initial and goal states. Example problems are presented like the 8-puzzle, missionaries and cannibals, cryptarithmetic etc. Generic state space search is formalized using a graph of nodes and operators. Key procedures like expand, goal test and queueing functions are discussed. Bookkeeping, search tree issues and ways to evaluate strategies are also covered at a high level.
The document provides information on various topics related to artificial intelligence including:
- Examples of intelligence such as solving puzzles, performing complex math problems quickly, and following rules.
- Definitions of AI from early researchers such as John McCarthy who coined the term, and descriptions of AI as the study of intelligent behavior in machines.
- Key areas of AI research and applications such as game playing, reasoning, learning, robotics, and machine learning.
- Approaches to problem solving in AI like state space search, knowledge representation, and using heuristics to guide searches.
Ch 2 State Space Search - slides part 1.pdfKrishnaMadala1
This document discusses problem solving through state space search. It explains that state space search involves representing a problem as an initial state, goal state, set of actions that can transform one state into another, and the set of all possible states. The document provides examples of applying state space search to problems like the missionaries and cannibals problem and the 8-queens puzzle. It also discusses strategies for controlling the order of applying actions during the search.
The document discusses the physical symbol system hypothesis in artificial intelligence. It states that cognition involves information processing through either physical symbol systems like Turing machines or connectionism/neural networks. A physical symbol system is necessary and sufficient for intelligent action if it can manipulate and represent symbols. The document then discusses defining problems as state spaces, representing knowledge for AI techniques, and using control strategies like search to systematically solve problems.
This document discusses state space representation in artificial intelligence. It provides examples of how state space representation can be used to model problems. Specifically, it describes:
1) The water jug problem, where the goal is to fill a 4 gallon jug with 2 gallons using only a 3 gallon jug. The initial and goal states are defined along with the possible state transitions.
2) Production rules for solving the water jug problem by pouring water between the jugs or emptying jugs.
3) The step-by-step solution to the water jug problem by applying the production rules to reach the goal state of filling the 4 gallon jug with 2 gallons.
This document discusses state space representation in artificial intelligence. It provides examples of how state space representation can be used to model problems. Specifically, it describes:
1) The water jug problem, where the goal is to fill a 4 gallon jug with 2 gallons using only a 3 gallon and 4 gallon jug.
2) It defines the initial state, goal state, and production rules to model the problem as transitions between states.
3) It then shows the step-by-step application of the rules to reach the goal state of filling the 4 gallon jug with 2 gallons.
1. The document discusses various AI techniques and problems. It defines AI technique as a method that exploits knowledge represented to capture generalizations, be understood by people, be easily modified, and be used in many situations.
2. It provides examples of common AI problems like tic-tac-toe, the water jug problem, various puzzles, and language understanding.
3. It then discusses problem solving and representation, defining key concepts like states, state space, operators, initial and goal states. It outlines general problem solving steps and state space representation.
This document discusses state space search techniques and heuristic search algorithms. It begins by defining problems as state space searches and describing how to represent problems as state spaces. It then discusses uninformed search techniques like breadth-first search and depth-first search. Next, it covers heuristic search techniques and algorithms like hill climbing, best-first search, and A*. It provides examples to illustrate hill climbing and discusses issues that can arise like local maxima. Finally, it summarizes generate-and-test and steepest-ascent hill climbing algorithms. In summary, the document outlines different search strategies and algorithms that can be used to solve problems modeled as state space searches.
Unit 1 Fundamentals of Artificial Intelligence-Part II.pptxDrYogeshDeshmukh1
The document discusses production systems in artificial intelligence. It defines production systems as having four basic components: a set of rules, a database of facts, a control strategy, and a rule firing module. The rules operate on the knowledge database by matching preconditions to facts in the database. If a precondition is satisfied, the rule can fire and change the database. The control system chooses which rule to apply. An example of using production rules to solve the water jug problem is provided, along with the representation of states and operators. Characteristics of production systems like simplicity, modularity, and knowledge-intensity are described. Different types of production systems are defined.
This document outlines steps for developing problem solving skills. It begins with an introduction and agenda, then defines the problem solving cycle which includes defining the problem, setting objectives, generating solutions, evaluating solutions, resolving the problem, and examining results. Each step is then explained in more detail. The importance of problem solving skills for careers like engineering is discussed, and the document concludes with self-assessment questions and examples to practice problem solving.
The document discusses various aspects of problem solving and production systems including:
- Problem characteristics like decomposability and recoverability impact the appropriate problem solving approach.
- Production systems consist of rules, databases, and a control strategy to apply rules.
- Well-designed heuristics can efficiently guide search toward solutions without exploring all possibilities.
- Different problem types like classification and design are suited to different control strategies like proposing and refining solutions.
Planning involves finding a sequence of actions that achieves a goal state from an initial state. It can be formulated as a state-space search problem. Progression planners search forward from the initial state, while regression planners search backward from the goal state. Partial-order planning delays commitments to action ordering, allowing more flexible plans. Planning graphs compactly represent interactions between actions and can provide better heuristics for search.
This document discusses various problem solving techniques through search. It begins with an introduction to problem representation, problem solving through search, and examples like the 8-puzzle and missionaries and cannibals problem. It then covers search methods and algorithms like breadth-first search, depth-first search, and A* search. Key concepts discussed include problem states, operators, initial states, goals, and search strategies. Real-world problems are abstracted and represented as states, operators, and paths for solving through search techniques.
The document discusses general problem solving in artificial intelligence. It defines key concepts like problem space, state space, operators, initial and goal states. Problem solving involves searching the state space to find a path from the initial to the goal state. Different search algorithms can be used, like depth-first search and breadth-first search. Heuristic functions can guide searches to improve efficiency. Constraint satisfaction problems are another class of problems that can be solved using techniques like backtracking.
ARTIFICIAL INTELLIGENCE PROBLEM SOLVING AND SEARCHJeff Brooks
1. The document discusses problem solving and search in artificial intelligence. It defines what a problem is and describes how many problems can be solved through search by exploring alternatives in a search space.
2. It provides examples of search problems like the 8 puzzle problem, maze problems, and the traveling salesman problem (TSP).
3. The document describes how to model problems, represent search spaces as graphs, and find solutions as paths between initial and goal states in the graph. It also covers uninformed and informed search strategies like depth-first search, breadth-first search, and iterative deepening.
The document provides an overview of problem spaces and problem solving through searching techniques used in artificial intelligence. It defines a problem space as a set of states and connections between states to represent a problem. Search strategies for finding solutions include breadth-first search, depth-first search, and heuristic search. Real-world problems discussed that can be solved through searching include route finding, layout problems, task scheduling, and the water jug problem is presented as a toy problem example.
1. The document discusses defining problems as state space searches which involves representing the problem as a graph with nodes as states and edges as operators to transition between states.
2. It provides examples of representing chess and the water jug problem as state space searches, defining the initial states, goal states, and production rules for the possible state transitions.
3. Search algorithms like breadth-first search and depth-first search are described for systematically exploring the state space to find a solution path from start to goal.
The document describes various problem solving concepts including defining a problem, representing the problem space, and searching for solutions. It provides examples of well-defined problems like the 8-puzzle, water jug problem, and missionaries and cannibals problem. It also discusses uninformed search strategies like breadth-first search that search the problem space without heuristics to guide the search. Breadth-first search expands the shallowest nodes first and has guarantees of completeness but not optimality.
The document discusses various concepts related to defining and solving problems using state space representation and search algorithms. It defines a problem as any task or goal that can be represented precisely using an initial state, goal state, and applicable rules. Search algorithms like breadth-first search and depth-first search are described for systematically exploring the problem space. Heuristic search methods are discussed which use domain knowledge to guide the search.
The document discusses problem solving techniques in artificial intelligence. It covers defining problems in a state space, using production systems to represent search spaces, and different search algorithms like breadth-first, depth-first, and heuristic search. It also discusses characteristics of problems that influence which techniques are best suited, such as decomposability, predictability, and interaction requirements.
The document discusses various concepts related to defining and solving problems using state space and search algorithms. It defines a problem as any task or goal that can be represented precisely using a state space with initial and goal states. Production systems use rules to search this space and find solutions. Common search algorithms like breadth-first, depth-first, and heuristic search are described. Characteristics of problems like decomposability, predictability, and interaction help determine the best approach.
Ch 2 State Space Search - slides part 1.pdfKrishnaMadala1
This document discusses problem solving through state space search. It explains that state space search involves representing a problem as an initial state, goal state, set of actions that can transform one state into another, and the set of all possible states. The document provides examples of applying state space search to problems like the missionaries and cannibals problem and the 8-queens puzzle. It also discusses strategies for controlling the order of applying actions during the search.
The document discusses the physical symbol system hypothesis in artificial intelligence. It states that cognition involves information processing through either physical symbol systems like Turing machines or connectionism/neural networks. A physical symbol system is necessary and sufficient for intelligent action if it can manipulate and represent symbols. The document then discusses defining problems as state spaces, representing knowledge for AI techniques, and using control strategies like search to systematically solve problems.
This document discusses state space representation in artificial intelligence. It provides examples of how state space representation can be used to model problems. Specifically, it describes:
1) The water jug problem, where the goal is to fill a 4 gallon jug with 2 gallons using only a 3 gallon jug. The initial and goal states are defined along with the possible state transitions.
2) Production rules for solving the water jug problem by pouring water between the jugs or emptying jugs.
3) The step-by-step solution to the water jug problem by applying the production rules to reach the goal state of filling the 4 gallon jug with 2 gallons.
This document discusses state space representation in artificial intelligence. It provides examples of how state space representation can be used to model problems. Specifically, it describes:
1) The water jug problem, where the goal is to fill a 4 gallon jug with 2 gallons using only a 3 gallon and 4 gallon jug.
2) It defines the initial state, goal state, and production rules to model the problem as transitions between states.
3) It then shows the step-by-step application of the rules to reach the goal state of filling the 4 gallon jug with 2 gallons.
1. The document discusses various AI techniques and problems. It defines AI technique as a method that exploits knowledge represented to capture generalizations, be understood by people, be easily modified, and be used in many situations.
2. It provides examples of common AI problems like tic-tac-toe, the water jug problem, various puzzles, and language understanding.
3. It then discusses problem solving and representation, defining key concepts like states, state space, operators, initial and goal states. It outlines general problem solving steps and state space representation.
This document discusses state space search techniques and heuristic search algorithms. It begins by defining problems as state space searches and describing how to represent problems as state spaces. It then discusses uninformed search techniques like breadth-first search and depth-first search. Next, it covers heuristic search techniques and algorithms like hill climbing, best-first search, and A*. It provides examples to illustrate hill climbing and discusses issues that can arise like local maxima. Finally, it summarizes generate-and-test and steepest-ascent hill climbing algorithms. In summary, the document outlines different search strategies and algorithms that can be used to solve problems modeled as state space searches.
Unit 1 Fundamentals of Artificial Intelligence-Part II.pptxDrYogeshDeshmukh1
The document discusses production systems in artificial intelligence. It defines production systems as having four basic components: a set of rules, a database of facts, a control strategy, and a rule firing module. The rules operate on the knowledge database by matching preconditions to facts in the database. If a precondition is satisfied, the rule can fire and change the database. The control system chooses which rule to apply. An example of using production rules to solve the water jug problem is provided, along with the representation of states and operators. Characteristics of production systems like simplicity, modularity, and knowledge-intensity are described. Different types of production systems are defined.
This document outlines steps for developing problem solving skills. It begins with an introduction and agenda, then defines the problem solving cycle which includes defining the problem, setting objectives, generating solutions, evaluating solutions, resolving the problem, and examining results. Each step is then explained in more detail. The importance of problem solving skills for careers like engineering is discussed, and the document concludes with self-assessment questions and examples to practice problem solving.
The document discusses various aspects of problem solving and production systems including:
- Problem characteristics like decomposability and recoverability impact the appropriate problem solving approach.
- Production systems consist of rules, databases, and a control strategy to apply rules.
- Well-designed heuristics can efficiently guide search toward solutions without exploring all possibilities.
- Different problem types like classification and design are suited to different control strategies like proposing and refining solutions.
Planning involves finding a sequence of actions that achieves a goal state from an initial state. It can be formulated as a state-space search problem. Progression planners search forward from the initial state, while regression planners search backward from the goal state. Partial-order planning delays commitments to action ordering, allowing more flexible plans. Planning graphs compactly represent interactions between actions and can provide better heuristics for search.
This document discusses various problem solving techniques through search. It begins with an introduction to problem representation, problem solving through search, and examples like the 8-puzzle and missionaries and cannibals problem. It then covers search methods and algorithms like breadth-first search, depth-first search, and A* search. Key concepts discussed include problem states, operators, initial states, goals, and search strategies. Real-world problems are abstracted and represented as states, operators, and paths for solving through search techniques.
The document discusses general problem solving in artificial intelligence. It defines key concepts like problem space, state space, operators, initial and goal states. Problem solving involves searching the state space to find a path from the initial to the goal state. Different search algorithms can be used, like depth-first search and breadth-first search. Heuristic functions can guide searches to improve efficiency. Constraint satisfaction problems are another class of problems that can be solved using techniques like backtracking.
ARTIFICIAL INTELLIGENCE PROBLEM SOLVING AND SEARCHJeff Brooks
1. The document discusses problem solving and search in artificial intelligence. It defines what a problem is and describes how many problems can be solved through search by exploring alternatives in a search space.
2. It provides examples of search problems like the 8 puzzle problem, maze problems, and the traveling salesman problem (TSP).
3. The document describes how to model problems, represent search spaces as graphs, and find solutions as paths between initial and goal states in the graph. It also covers uninformed and informed search strategies like depth-first search, breadth-first search, and iterative deepening.
The document provides an overview of problem spaces and problem solving through searching techniques used in artificial intelligence. It defines a problem space as a set of states and connections between states to represent a problem. Search strategies for finding solutions include breadth-first search, depth-first search, and heuristic search. Real-world problems discussed that can be solved through searching include route finding, layout problems, task scheduling, and the water jug problem is presented as a toy problem example.
1. The document discusses defining problems as state space searches which involves representing the problem as a graph with nodes as states and edges as operators to transition between states.
2. It provides examples of representing chess and the water jug problem as state space searches, defining the initial states, goal states, and production rules for the possible state transitions.
3. Search algorithms like breadth-first search and depth-first search are described for systematically exploring the state space to find a solution path from start to goal.
The document describes various problem solving concepts including defining a problem, representing the problem space, and searching for solutions. It provides examples of well-defined problems like the 8-puzzle, water jug problem, and missionaries and cannibals problem. It also discusses uninformed search strategies like breadth-first search that search the problem space without heuristics to guide the search. Breadth-first search expands the shallowest nodes first and has guarantees of completeness but not optimality.
The document discusses various concepts related to defining and solving problems using state space representation and search algorithms. It defines a problem as any task or goal that can be represented precisely using an initial state, goal state, and applicable rules. Search algorithms like breadth-first search and depth-first search are described for systematically exploring the problem space. Heuristic search methods are discussed which use domain knowledge to guide the search.
The document discusses problem solving techniques in artificial intelligence. It covers defining problems in a state space, using production systems to represent search spaces, and different search algorithms like breadth-first, depth-first, and heuristic search. It also discusses characteristics of problems that influence which techniques are best suited, such as decomposability, predictability, and interaction requirements.
The document discusses various concepts related to defining and solving problems using state space and search algorithms. It defines a problem as any task or goal that can be represented precisely using a state space with initial and goal states. Production systems use rules to search this space and find solutions. Common search algorithms like breadth-first, depth-first, and heuristic search are described. Characteristics of problems like decomposability, predictability, and interaction help determine the best approach.
Elevate Your Nonprofit's Online Presence_ A Guide to Effective SEO Strategies...TechSoup
Whether you're new to SEO or looking to refine your existing strategies, this webinar will provide you with actionable insights and practical tips to elevate your nonprofit's online presence.
A Visual Guide to 1 Samuel | A Tale of Two HeartsSteve Thomason
These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
🔥🔥🔥🔥🔥🔥🔥🔥🔥
إضغ بين إيديكم من أقوى الملازم التي صممتها
ملزمة تشريح الجهاز الهيكلي (نظري 3)
💀💀💀💀💀💀💀💀💀💀
تتميز هذهِ الملزمة بعِدة مُميزات :
1- مُترجمة ترجمة تُناسب جميع المستويات
2- تحتوي على 78 رسم توضيحي لكل كلمة موجودة بالملزمة (لكل كلمة !!!!)
#فهم_ماكو_درخ
3- دقة الكتابة والصور عالية جداً جداً جداً
4- هُنالك بعض المعلومات تم توضيحها بشكل تفصيلي جداً (تُعتبر لدى الطالب أو الطالبة بإنها معلومات مُبهمة ومع ذلك تم توضيح هذهِ المعلومات المُبهمة بشكل تفصيلي جداً
5- الملزمة تشرح نفسها ب نفسها بس تكلك تعال اقراني
6- تحتوي الملزمة في اول سلايد على خارطة تتضمن جميع تفرُعات معلومات الجهاز الهيكلي المذكورة في هذهِ الملزمة
واخيراً هذهِ الملزمة حلالٌ عليكم وإتمنى منكم إن تدعولي بالخير والصحة والعافية فقط
كل التوفيق زملائي وزميلاتي ، زميلكم محمد الذهبي 💊💊
🔥🔥🔥🔥🔥🔥🔥🔥🔥
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
A Free 200-Page eBook ~ Brain and Mind Exercise.pptxOH TEIK BIN
(A Free eBook comprising 3 Sets of Presentation of a selection of Puzzles, Brain Teasers and Thinking Problems to exercise both the mind and the Right and Left Brain. To help keep the mind and brain fit and healthy. Good for both the young and old alike.
Answers are given for all the puzzles and problems.)
With Metta,
Bro. Oh Teik Bin 🙏🤓🤔🥰
How to Setup Default Value for a Field in Odoo 17Celine George
In Odoo, we can set a default value for a field during the creation of a record for a model. We have many methods in odoo for setting a default value to the field.
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...indexPub
The recent surge in pro-Palestine student activism has prompted significant responses from universities, ranging from negotiations and divestment commitments to increased transparency about investments in companies supporting the war on Gaza. This activism has led to the cessation of student encampments but also highlighted the substantial sacrifices made by students, including academic disruptions and personal risks. The primary drivers of these protests are poor university administration, lack of transparency, and inadequate communication between officials and students. This study examines the profound emotional, psychological, and professional impacts on students engaged in pro-Palestine protests, focusing on Generation Z's (Gen-Z) activism dynamics. This paper explores the significant sacrifices made by these students and even the professors supporting the pro-Palestine movement, with a focus on recent global movements. Through an in-depth analysis of printed and electronic media, the study examines the impacts of these sacrifices on the academic and personal lives of those involved. The paper highlights examples from various universities, demonstrating student activism's long-term and short-term effects, including disciplinary actions, social backlash, and career implications. The researchers also explore the broader implications of student sacrifices. The findings reveal that these sacrifices are driven by a profound commitment to justice and human rights, and are influenced by the increasing availability of information, peer interactions, and personal convictions. The study also discusses the broader implications of this activism, comparing it to historical precedents and assessing its potential to influence policy and public opinion. The emotional and psychological toll on student activists is significant, but their sense of purpose and community support mitigates some of these challenges. However, the researchers call for acknowledging the broader Impact of these sacrifices on the future global movement of FreePalestine.
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
2. SYLLABUS
• Introduction: What is AI
• The foundations of AI
• History and applications
• Production systems.
• Structures and strategies for state space search.
• Informed and Uninformed searches.
4. The AI problems
• Mundane Tasks
– Perception
• Vision
• Speech
– Natural Language
• Understanding
• Generation
• Translation
– Commonsense
reasoning
– Robot control
• Formal Taks
– Games
– Chess
– Backgammon
– Checkers
• Mathematics
– Geometry
– Logic
– Integral calculus
– Proving properties of
programs
• Expert Tasks
– Engineering
– Design
– Fault Finding
– Manufacturing
planning
• Scientific Analysis
• Medical Diagnosis
• Financial Analysis
4
Prepared by Sharika T R, SNGCE
5. Cont..
• AI is a system that acts like human beings
– 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.
– Computer vision:To perceive objects.
– Robotics: To manipulate objects and move about. 5
Prepared by Sharika T R, SNGCE
6. Cont..
• AI is a system that thinks like human beings
• AI is a system that thinks rationally
– For a given set of correct premises, it is possible to yield new
conclusions.
• AI is a system that acts rationally
6
Prepared by Sharika T R, SNGCE
7. Cont..
• 2 most fundamental concerns of AI researchers
• Knowledge representation
– It addresses the problem of capturing the full range of
knowledge required for intelligent behavior in a formal
language,
– i.e. One suitable for computer manipulation.
– Eg. predicate calculus, LISP, Prolog
• Search
– It is a problem solving technique that systematically explores a
space of problem states, ie, successive and alternative stages
in the problem solving process. 7
Prepared by Sharika T R, SNGCE
8. AI Application Areas
• Game Playing
• Heuristics
• Automated Reasoning and Theorem Proving
• Expert Systems
• Natural Language Understanding and Semantic Modeling
• Modeling Human Performance
8
Prepared by Sharika T R, SNGCE
9. Languages and Environments for AI
• Programming environments include
– knowledge structuring techniques such as
– object oriented programming and
– expert systems frameworks.
• High level languages such as
– Lisp, and
– Prolog support modular development.
9
Prepared by Sharika T R, SNGCE
10. Problems, Problem Space And Search
• To build a system to solve a particular problem
– Define the problem precisely:
• precise specification of what the initial situation will be as well as what
final situation constitute acceptable solution to the problem
– Analyse the problem
• a few very important feature can have an immense impact on
appropriateness of various possible technique for solving the problem
– Isolate and represent the task knowledge
• that is necessary to solve the problem
– Choose the best problem solving technique and apply it to
particular problem.
10
Prepared by Sharika T R, SNGCE
11. Defining problem as a state space search
• Build a program to PLAY CHESS
11
Prepared by Sharika T R, SNGCE
12. Cont..
• To build a program
– Specify the starting position of the chess board
– The rules that define the legal moves
– The board positions that represent a win for one side or other
• Set of rules consisting of two parts:
– A left side that serves as a pattern to be matched against the
current board position
– A right side that describe the change to be made to the board
position to reflect the move
12
Prepared by Sharika T R, SNGCE
14. • state space representation forms the basics of most of the
AI methods Its Structure is:
– It allows for a formal definition of a problem as the need to
convert some given situation into some desired situation using
a set of permissible operations
– It permits us to define the process of solving a particular
problem as a combination of known techniques and
– search the general technique of exploring the space to try to
find some path from the current state to goal state.
14
Prepared by Sharika T R, SNGCE
15. Water Jug Problem
4 gallon Jug 3 gallon jug
no measuring markers
15
Prepared by Sharika T R, SNGCE
16. Cont..
You are given two jugs, a 4-gallon one and a 3 gallonone.
Neither has any measuring markers on it.There is a pump
that can be used to fill the jugs with water.
How can you get exactly 2gallon of water into 4 gallon jar?
16
Prepared by Sharika T R, SNGCE
17. Cont..
• state space
– set of ordered pair of integers(x,y) such that
– x=0,1,2,3 or 4 and y=0,1,2 or 3.
– x represent number of gallon of water in the 4 gallon jug
– y represent number of gallon of water in the 3 gallon jug
• Start state is (0,0).
• Goal state is (2,n) n can be any value.
17
Prepared by Sharika T R, SNGCE
18. • Production rules
1 (x,y) -----> (4,y)If x<4 Fill the 4 gallon jug
2 (x,y) -----> (x,3)If y<3 Fill the 3 gallon jug
3 (x,y) -----> (x-d,y)If x>0 Pour some water out of 4 gallon jug
4 (x,y) -----> (x,y-d)If y>0 Pour some water out of 3 gallon jug
5 (x,y) -----> (0,y)If x>0 Empty the 4 gallon jug on ground
6 (x,y) -----> (x,0)If y>0 Empty the 3 gallon jug on ground
18
Prepared by Sharika T R, SNGCE
19. Cont..
7 (x,y) -----> ( 4, y-(4-x) ) If
x+y>=4 and y>0
Pour water from 3 gallon jug into 4 gallon jug until 4
gallon jug is fill
8 (x,y) -----> ( x-(3-y), 3 ) If x+y>=4
and y>0
Pour water from 4 gallon jug into 3 gallon jug until 3
gallon jug is fill
9 (x,y) -----> (x+y,0) If x+y<=4 and
y>0
Pour all water from 3 gallon jug into 4 gallon jug
10 (x,y) -----> (0,x+y) If x+y<=4 and
y>0
Pour all water from 4 gallon jug into 3 gallon jug
11 (0,2) -----> (2,0) If x+y<=4 and
y>0
Pour the 2 gallon from 3 gallon jug into 4 gallon jug.
12 (2,y) -----> (0,y) If x+y<=4 and
y>0
Empty the 2 gallon in the 4 gallon on the ground
19
Prepared by Sharika T R, SNGCE
20. Solution
Gallon in 4 gallon
Jug
Gallon in 3 gallon
Jug
Rule Applied
20
Prepared by Sharika T R, SNGCE
21. Cont..
• Production System
– A set of rules each contsisting of a
• left side that determines the applicability of the rule and
• right side that describes the operation to be performed if rule is applied.
– One or more knowledge/database that contain whatever
information is appropriate for the particular task.
– A control stratergy that specifies
• the order in which the rules will be compared to the database and
• a way of resolving the conflicts that arise when several rules match at
once
– A rule applier.
21
Prepared by Sharika T R, SNGCE
22. Problem Charateristics
• 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 path?
• What is the Role of Knowledge
• Does the task require interaction with a person?
22
Prepared by Sharika T R, SNGCE
23. Is the problem Decomposable?
• Decomposable problems
• Non Decomposable problems
23
Prepared by Sharika T R, SNGCE
24. 1. Decomposable problems
• can solve this problem by
breaking it down into three
smaller problems
• each of which we can then solve
by using a small collection of
specific rules.
• problem decomposition
24
Prepared by Sharika T R, SNGCE
28. Cont..
• Regardless of which one we do first we will not be able to
do the second as we had planned.
• In this problem the two sub problems are not
independent.
• They interact and those interactions must be considered
in order to arrive at a solution for entire problem.
28
Prepared by Sharika T R, SNGCE
29. Can solution steps be ignored or undone?
• Here we can divide problems into 3 classes.
– Ignorable, in which solution steps can be ignored.
– Recoverable, in which solution steps can be undone.
– Irrecoverable, in which solution steps cannot be undone.
29
Prepared by Sharika T R, SNGCE
30. Ignorable Problem
• eg, Theorm Proving
• Suppose we are trying to prove a mathematical theorem.
• We proceed by first proving a lemma that we think will be
useful.
• Eventually we realize that the lemma is no help at all.
• Here the different steps in proving the theorem can be
ignored.
• Then we can start from another rule.
• The former can be ignored.
30
Prepared by Sharika T R, SNGCE
31. Recoverable Problems
• eg, 8 Puzzle
• 8-puzzle solver can keep track of the order in which
operations are performed so that the operations can be
undone one at a time if necessary.
31
Prepared by Sharika T R, SNGCE
32. Irrecoverable problems
• eg. Chess
• Suppose a chess playing program makes a stupid move
and realizes it a couple of moves later.
• It cannot simply play as though it had never made the
stupid move.
• Nor can it simply back up and start the game over from
that point.
• All it can do is to try to make the best of the current
situation and go from there.
32
Prepared by Sharika T R, SNGCE
33. Cont..
• Ignorable problems can be solved using a simple control
structure.
• Recoverable problems can be solved by a slightly more
complicated control strategy that does sometimes makes
mistakes.
• Irrecoverable problems will need to be solved by a system
that expends a great deal of effort making each decision
since the decision must be final.
33
Prepared by Sharika T R, SNGCE
34. Is the universe predictable?
• Certain outcome problems
• Uncertain outcome problems
34
Prepared by Sharika T R, SNGCE
35. Certain outcome problems
• 8 puzzle problem.
• Every time we make a move, we know exactly what will
happen.
• This means that it is possible to plan an entire sequence
of moves and be confident that we know what the
resulting state will be.
35
Prepared by Sharika T R, SNGCE
36. Uncertain outcome problems
• Bridge
– planning may not be possible.
– One of the decisions we will have to
make is which card to play on the first
trick.
– it is not possible to do such planning
with certainty since we cannot know
exactly where all the cards are or
what the other players will do on their
turns.
36
Prepared by Sharika T R, SNGCE
37. 4.Is a good solution Absolute or relative
• Any Path Problem
• Best Path Problem
37
Prepared by Sharika T R, SNGCE
38. Any path problems
• Is a good solution Absolute or relative
• Any path problems
– 1. Marcus was a man.
– 2. Marcus was a Pompean.
– 3. Marcus was born in 40 A. D.
– 4. all men are mortal.
– 5. All pompeans died when the volcano erupted in 79 A. D.
– 6. No mortal lives longer than 150 years.
– 7. It is now 1991 A. D.
• Suppose we ask the question. “Is Marcus alive?”. 38
Prepared by Sharika T R, SNGCE
39. Solutions Axiom
1 Marcus was a man. 1
4 All men are mortal. 4
3 Marcus was born in 40 A.D. 3
7 It is now 2017 A. D. 7
9 Marcus’ age is 1977 years. 3,7
6 no mortal lives longer than 150 years. 6
10 Marcus is dead. 8,6,9
39
Prepared by Sharika T R, SNGCE
40. Best path problems
• Traveling salesman problem
• Best path problems are computationally harder than any
path problems.
• Any path problem can often be solved in a reasonable
amount of time by using heuristics that suggest good path
to explore.
40
Prepared by Sharika T R, SNGCE
41. 5.Is the solution a state or path?
• Problems whose solution is a state of the world. eg.
Natural language understanding. eg,
‘ The bank president ate a dish of pasta salad with the
fork’.
– Since all we are interested in is the answer to the question, it
does not matter which path we follow.
• Problems whose solution is a path to a state?
– Eg. Water jug problem
– In water jug problem, it is not sufficient to report that we have
solved the problem and that the final state is (2,0).
– For this kind of problem, what we really must report is not the
final state, but the path that we found to that state.
41
Prepared by Sharika T R, SNGCE
42. 6. What is the Role of Knowledge
• Problems for which a lot of knowledge is important only to
constrain the search for a solution.
– Eg. Chess
– Just the rules for determining the legal moves and some simple
control mechanism that implements an appropriate search
procedure
• Problems for which a lot of knowledge is required even to
be able to recognize a solution.
– Eg. News paper story understanding
42
Prepared by Sharika T R, SNGCE
43. 7. Does the task require interaction with a person?
• Solitary problems
– Here the computer is given a problem description and produces
an answer with no intermediate communication and with no
demand for an explanation for the reasoning process.
– Consider the problem of proving mathematical theorems. If
• All we want is to know that there is a proof.
• The program is capable of finding a proof by itself.
– Then it does not matter what strategy the program takes to find
the proof.
43
Prepared by Sharika T R, SNGCE
44. Cont..
• Conversational problems
– In which there is intermediate communication between a
person and the computer, either to provide additional
assistance to the computer or to provide additional information
to the user.
• Eg. Suppose we are trying to prove some new, very difficult theorem.
• Then the program may not know where to start.
• At the moment, people are still better at doing the high level strategy
required for a proof.
• So the computer might like to be able to ask for advice.
• To exploit such advice, the computer’s reasoning must be analogous
to that of its human advisor, at least on a few levels. 44
Prepared by Sharika T R, SNGCE
45. SEARCHING
• search algorithm takes a problem as input and returns the solution
in the form of an action sequence.
• Once the solution is found the execution phase.
• After formulating a goal and problem to solve the agent cells a
search procedure to solve it.
• A problem can be defined by 5 components.
a) The initial state: The state from which agent will start.
b) The goal state: The state to be finally reached.
c) The current state: The state at which the agent is present after starting
from the initial state.
d) Successor function: It is the description of possible actions and their
outcomes.
e) Path cost: It is a function that assigns a numeric cost to each path.
47. Uninformed Search Strategies
• A problem determines the graph and the goal but not which path
to select from the frontier.
• This is the job of a search strategy.
• A search strategy specifies which paths are selected from the
frontier.
• Different strategies are obtained by modifying how the selection of
paths in the frontier is implemented.
• they that do not take into account the location of the goal. These
algorithms ignore where they are going until they find a goal and
report success.
– Depth-First Search
– Breadth-First Search
48. Informed Search
• A search using domain-specific knowledge.
• Suppose that we have a way to estimate how close a
state is to the goal, with an evaluation function.
• General strategy:
– expand the best state in the open list first.
– It's called a best-first search or ordered state-space search.
• In general the evaluation function is imprecise, which
makes the method a heuristic (works well in most cases).
• The evaluation is often based on empirical observations.
49. Control Stratergies
• Requirments
– cause motion
– it must be systematic
• Control stratergies that do not cause motion will never
lead to a solution.
• The requirement that a control stratergy be systemtic
corresponds to the need for global motion as well as for
local motion.
49
Prepared by Sharika T R, SNGCE
51. Breath First Search
• Construct a tree with initial state as its
root,
• generate all offspring of the root by
applying each of the applicable rules to
the initial state.
• Now for each leaf node, generate all its
sucessors by applying all the rules that
are appropriate.
• Continue this process until some rule
produces a goal state.
51
Prepared by Sharika T R, SNGCE
52. Breadth first Search Algorithm
1. Create a variable called NODE_LIST and set it to initial
state
2. Until a goal state is found or NODE_LIST is empty
a) Remove the first element from NODE_LIST and call it E. if
NODE_LIST was empty quit
b) For each way that each rule can match the state described in
E do
i. Apply the rule to generate a new state
ii. If the new state is a goal state, quit and return this state
iii. Otherwise add the new state to end of NODE_LIST
52
Prepared by Sharika T R, SNGCE
53. Depth first search
• Pursue a single branch of tree until a solution or until a
decision to determine the path is made.
• It makes sense to terminate a path if it reaches a dead
end. In such a case, backtracking occurs.
• The most recently created state from which alternative
moves are available will be revisited and a new state will
be created.
• This form of backtracking is called chronological
backtracking.
53
Prepared by Sharika T R, SNGCE
54. DFS Algorithm
1. if the initial state is a goal state, quit
and return success
2. Otherwise do the following until
success or failure is signaled:
a) Generate a successor E of the initial
state. If there are no more
successors,signal failure
b) Call Depth first search with E as the
initial state
c) If the success is returned, signal
success otherwise continue in the 54
Prepared by Sharika T R, SNGCE
55. Advantages of BFS
• Breath first search will not get trapped exploring a blind
alley.
– This contrasts with DFS which may follow a single, unfruitful
path for a very long time, before the path actually terminates in
a state that has no successors.
• If there is a solution then breath first search is guarenteed
to find it.
• Furthermore if there are multiple solutions, then a minimal
solution will be found.
55
Prepared by Sharika T R, SNGCE
56. Traveling Salesman Problem
A salesman has a list of cities each of which he must visit
exactly once.
There are direct roads between each pair of cities on the
list.
Find the route the salesman should follow for the shortest
possible round trip that both starts and finishes at any one
of the cities.
56
Prepared by Sharika T R, SNGCE
57. Cont..
• Strategy 1
– It would simply explore all possible path in the tree and return
the one with the shortest length.
– This approach will even work in practice for every short list of
cities.
– If there are N cities then the number of different path among
them is 1,2, . . . (N-1) or (N-1)!
– Not a feasible solution
57
Prepared by Sharika T R, SNGCE
58. Cont..
• Strategy
– branch and bound.
– Being generating complete paths keeping track of shortest path
found so far
– give up exploring any path as soon as its partial length become
greater than the shortest path found so far .
– Using this technique we are still guarenteed to find the shortest
path.
58
Prepared by Sharika T R, SNGCE
59. Heuristic Search
Heuristics are rule for choosing branches in a state space
that are most likely to lead to an acceptable problem
solution.
• Two basic solution:
– a problem may not have an exact solution
• eg, Medical diagnosis: A given set of symptoms may have several
possible causes, doctors use heuristics to choose the most likely
diagnosis and formulate a plan of treatment.
– A problem may have an exact solution, but the computational
cost of finding it may be prohibitive.
• A heuristic algorithm can defeat this combinational explosion and find
an acceptable solution.
59
Prepared by Sharika T R, SNGCE
60. Cont..
• Heuristic approach for travelling salesman problem
– arbitarily select a starting city
– To select the next city, look at all cities not yet visited and select
the one closest to the current city go to it next.
– Repeat step 2 until all cities have been visited
• executes in N2 time which is better than N!
60
Prepared by Sharika T R, SNGCE
63. Informed Search Technique
Generate and Test, Plan Generate and Test, Hill Climbing, Simulated
Annealing
63
Prepared by Sharika T R, SNGCE
64. Generate-and-Test Algorithm
1. Generate a possible solution.
For some problems, this means generating a particular point in the
problem space.
For others it means generating a path from a start state.
2. Test to see if this is actually a solution by comparing the
choosen point or the endpoint of the choosen path to the
set of acceptable goal states
3. If a solution has been found, quit. Otherwise return to
step 1.
64
Prepared by Sharika T R, SNGCE
65. Cont..
• If the generation of possible solution is done
systematically, then this procedure will find a solution
eventually if one exists.
• If the problem space is very large eventually may be a
very long time.
• It is a depth first search procedure since complete
solutions must be generated before they can be tested.
• It can also operate by generating solutions randomly but
then there is no guarentee that a solution will ever be
found.
• It is also known as British Museum Algorithm 65
Prepared by Sharika T R, SNGCE
67. Plan-Generate-Test
• Dendral uses plan-generate test
stratergy in which a planing process
that uses constraint-satisfaction
techniques creates list of recommended
and contraindicated substructures.
• The generate-and-test procedure then
uses those lists so that it can explore
only a fairly limited set of structures.
• Constrained in this way generate-and-
test procedure has proved highly
effective.
67
Prepared by Sharika T R, SNGCE
68. Cont..
• A major weakness of planning is that it often produces
somewhat inaccurate solutions since there is no feedback
from the world.
• But by using it only produce pieces of solution that will
then be exploited in the generate-and-test process, the
lack of detailed accuracy become unimportant.
68
Prepared by Sharika T R, SNGCE
69. Hill Climbing
• Generate-and-test + direction to move.
• Heuristic function to estimate how close a given state is to
a goal state.
• Hill climbing is a variant of generate and test in which
feedback from the test procedure is used to help the
generator decide which direction to move in the search
space.
69
Prepared by Sharika T R, SNGCE
70. Simple hill Climbing
1. Evaluate the initial state. If it is also a goal state, then
return it and quit. Otherwise continue with the initial state
as the current state
2. Loop until a solution is found or until there are no new
operators left to be applied in current state
a) Select an operator that has not yet been applied to the current
state and apply it to produce a new state
b) Evaluate the new state
i. If it is a goal state, then return it and quit
ii. If it is not a goal state but it is better than current state then make it
the current state
iii. If it is not better than current state then continue in the loop 70
Prepared by Sharika T R, SNGCE
71. Cont..
• The key difference between this algorithm and generate-
and-test is the use of an evaluation function as a way to
inject task specific knowledge into control proceed.
• In this algorithm we asked the relatively vague question,
‘Is one state better than another”.
71
Prepared by Sharika T R, SNGCE
72. Steepest Ascent Hill Climbing(Gradient Search)
1. Evaluate the initial state. If it is also goal state, then return it and
quit otherwise continue with the initial state as the current state.
2. Loop until a solution is found or until a complete iteration
produces no change to current state:
a. Let SUCC be a state such that any possible successor of the current
state will be better than SUCC
b. For each operator that applies to the current state do
i. Apply the operator or generate a new state
ii. Evaluate the new state. If it is a goal state, then return it and quit. If
not compare it to SUCC. If it is better, then set SUCC to this state. If
it is not better, leave SUCC alone.
iii. If the SUCC is better than current state, then set current state to
SUCC. 72
Prepared by Sharika T R, SNGCE
73. Cont..
• In steepest-ascent hill climbing we must consider all
permuatations of the initial state and choose the best.
• Basic and steepest hill climbing may terminate not by
finding a goal state but by getting to a state from which no
better states can be generated.
• This will happen if the program has reached either a local
maximum, plateau or ridge.
73
Prepared by Sharika T R, SNGCE
74. Local Maximum
• It is a state that is better
than all its neighbors but is
not better than sum other
states farther away.
74
Prepared by Sharika T R, SNGCE
75. Plateau
• flat area of the search
space in which a whole set
of neighboring states have
the same value.
• On a plateau it is not
possible to determine the
best direction in which to
move by making local
comparison
75
Prepared by Sharika T R, SNGCE
76. Ridge
• The orientation of the high
region, compared to the
set of available moves,
makes it impossible to
climb up.
• However, two moves
executed serially may
increase the height
76
Prepared by Sharika T R, SNGCE
77. Ways for dealing with these problems
• Backtrack to some earlier node and try going in a different
direction. This is a fairly good way of dealing with local
maxima.
• Make a big jump in some direction to try to get to a new
section of the search space. This is a good way of dealing
with plateaus.
• Apply two or more rules before doing the test. This
corresponds to moving in several directions at once. This
is a good way for dealing with ridges.
77
Prepared by Sharika T R, SNGCE
78. Local and Global Heuristics
• Hill Climbing is a local method
– it decide what to do next by looking only the immediate
consequence of its choice rather than by exhustively exploring
all the consequences
– it lacks guarentee that it will be effective
– looks only one move ahead not any further
78
Prepared by Sharika T R, SNGCE
79. Example
• Local Heuristic function:
– Add one point for every
block that is resting on the
thing it is supposed to be
resting on.
– Substract one point for
every block that is sitting on
wrong thing
– Goal Score=8
– Initial Score=4 (6-2)
79
Prepared by Sharika T R, SNGCE
80. Cont..
• Move A to table makes State
Score=6
• State Scores after moving H
– (a) = 4
– (b) =4
– (c)=4
• Local Maximum reached
• to solve this it is necessary to
disassemble a good local structure
because it is wrong global structure 80
Prepared by Sharika T R, SNGCE
81. • We could modify the heuristic function to make it better.
• Global Heuristic Function:
– For each block that has correct structure add one point for
every block in support structure
– For each block that has an incorrect support structure substract
one point for every block in existing support structure
• That makes
– INITIAL SCORE= -28
– FINAL SCORE= 28 (A=0,B=1,C=2...)
81
Prepared by Sharika T R, SNGCE
82. Cont..
• Moving A to table make
score= -21 (-22+1)
– since A do not have 7 wrong
blocks under it
– the same 3 states produce
following scores
• (a) = -28
• (b) = -16
• (c) = -15
– Now we can choose (c)
82
Prepared by Sharika T R, SNGCE
83. Cont..
• It is not always possible to construct such a perfect
heuristic function
• hill climbing can be very ineffective in a large, rough
problem space.
• it is useful when combined with other methods that get it
started in right general neighborhood
83
Prepared by Sharika T R, SNGCE
84. Simulated Annealing
• Simulated annealing is a variation of hill climbing in which
at the begining of the process, some downhill moves may
be made.
do enough exploration of whole space early on so that the
final solution is relatively insensitive to starting state
• This should lower the chances of getting caught at a local
maximum, a plateau, or a ridge.
84
Prepared by Sharika T R, SNGCE
85. Cont..
• Two notational changes
1. We use the term objective function in the place of the
term heuristic function
2. We attempt to minimize rather than maximize the
value of the objective function
valley descending rather than hill climbing
• Goal of this process is to produce a minimal-energy final
state.
• objective function= energy level
85
Prepared by Sharika T R, SNGCE
86. Physical Anealing
• Metals melted and then gradually cooled until some solid
state is reached
• But there is some probability that a transition to higher
energy state will occur,
• k describes the correspondence between the units of
temprature and the units of energy 86
Prepared by Sharika T R, SNGCE
88. Cont..
• probability of a large uphill move is lower than probability
of a small one
probability that an uphill move will be made
decreases as temprature decreases.
• Rate at which system is cooled is called the annealing
schedule
– if cooling occurs too rapidly stable regions of high energy will
form ie, local minimum will be reached
– if cooling occur slowly uniform crystelline structure ie, global
minimum will be reached
– if too slow time is wasted
88
Prepared by Sharika T R, SNGCE
89. Cont..
• Explore successors wildly randomly==>High Temperature
• As time goes by explore less wildly ==> Cool Down
• Until her's a time where things settle ==> Cold
89
Prepared by Sharika T R, SNGCE
90. Simulated Annealing from Physical
1. Evaluate the initial state. If it is also a goal state, then
return it and quit. Otherwise continue with the initial state
as the current state.
2. Initialize BEST_SO_FAR to the current state
3. Initialize T according to the annealing schedule
90
Prepared by Sharika T R, SNGCE
91. Cont..
4. Loop until a solution is found or until there are no new operators
left to be applied in the current state.
a) Select an operator that has not yet been applied to the current state and apply
it to produce a new state
b) Evaluate the new state compute (Value of current) - (value of new )
• If the new state is a goal state, then return it and quit
• If it is not a goal state but it is better then make it the current state. Also set
BEST_SO_FAR to this new state
• If it is not better than the current state, then make it the current state with
probability p’ as defined above. This step is usually implemented by invoking
a random number generator to produce a number in the range [0,1]. if that
number is less than p’, then the move is accepted. Otherwise do nothing.
c) revise T as necessary according to the annealing schedule.
5. Return BEST_SO_FAR as the answer.
91
Prepared by Sharika T R, SNGCE
92. Cont..
• The annealing schedule has three components.
1. the initial value to be used for temprature.
2. the criteria that will be used to decide when the temprature of the system
should be reduced.
3. the amount by which the temprature will be reduced each time it is
changed.
4. There may also be a fourth component of the schedule, when to quit.
• T starts out high and gradually decreases towards 0.
• The higher the temprature the more likely it is that a bad move
can be made.
• As T tends to zero, this probability tends to zero and simulated
annealing become more like hill climbing 92
Prepared by Sharika T R, SNGCE