Unit 1- Part 1.pptx about basic of Artificial intelligence

Providing basics of ai

1. 21CSC206T Artificial Intelligence References :   Stuart Russel and Peter Norvig , “Artificial Intelligence: A Modern Approach”, Fourth Edition, Pearson Education, 2020. 2.   Parag Kulkarni, Prachi Joshi, Artificial Intelligence –Building Intelligent Systems, 1st ed., PHI learning, 2015.

2. Sl.No Component Type Marks 1 Cycle Test-I Written Test 10 2. Quiz/Puzzles 5 3. AWS online Course Completion (Machine Learning Foundation) 10 2 Cycle Test-II Written Test 10 2. Quiz/Puzzles 5 3. Hackerank - 5 Questions 10 3 Hackathon / Group Activity Global Challenge / Hackathons/ Ideathons / Makethons /Any AI Technical Competitions including conference presentations/ Samsung Prism 5 2. Group Activity (Poster Presentation) 5 Total Marks 60 Assessment Plan

3. Unit 1- Part 1 AI techniques, Problem solving with AI, AI Models, Data acquisition and learning aspects in AI Problem solving- Problem solving process, formulating problems

4. Introduction to AI Real Worlds Examples Automatic Toll Collection Booth Loading Vehicles Costume suggestion

5. Artificial Intelligence AI helps in taking decisions with reduced human interventions Automated climate control in a car Self driving car AI holistically includes, learning, searching and problem solving. The purpose of AI is to make machine intelligent and enable the machine to solve the problems . "AI is the study of how to make computers do things which, at the moment, people do better“ - Rick & Knight

6. Definition of AI Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally HUMAN RATIONAL Artificial Intelligence is defined in several ways, few of them are categorized in two dimensions as shown below: THOUGHT PROCESS AND REASONING BEHAVIOUR A system is rational if it does the “right thing,” given what it knows . A human-centered approach must be in part an empirical science, involving observations and hypotheses about human behavior. A rationalist approach involves a combination of mathematics and engineering.

7. Acting Humanly - The Turing Test approach “The art of creating machines that perform functions that require intelligence when performed by people.” (Kurzweil) “The study of how to make computers do things at which, at the moment, people are better.” (Rich and Knight) The Turing Test, proposed by Alan Turing (1950), was designed to provide an operational definition of intelligence. A computer passes the test if a human interrogator, after posing some written questions, cannot tell whether the written responses come from a person or from a computer. The computer would need to possess the following capabilities: 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 (Seeing) Robotics to manipulate objects and move about (Acting)

8. Thinking Humanly -The cognitive modeling approach “ The exciting new effort to make computers think . . . machines with minds, in the full and literal sense.” ( Haugeland , 1985) “[The automation of] activities that we associate with human thinking, activities such as decision-making, problem solving, earning . . .” (Bellman, 1978) Humans as observed from ‘inside’ How do we know how humans think? Introspection vs. psychological experiments COGNITIVE SCIENCE The interdisciplinary field of cognitive science brings together computer models from AI and experimental techniques from psychology to construct precise and testable theories of the human mind.

9. Thinking Rationally - The “laws of thought” approach “The study of mental faculties through the use of computational models.”( Charniak and McDermott, 1985) “The study of the computations that make it possible to perceive, reason, and act.” (Winston, 1992) Logic can’t express everything (e.g. uncertainty) Logical approach is often not feasible in terms of computation time (needs ‘guidance’) Not all intelligent behavior controlled by logic Acting Rationally -The rational agent approach Rational behavior: doing the right thing The right thing : that which is expected to maximize goal achievement, given the available information An agent is just something that acts . Giving answers to questions is ‘acting’. A rational agent is one that acts so as to achieve the best outcome or, when there is uncertainty, the best expected outcome . “Computational Intelligence is the study of the design of intelligent agents.” (Poole et al., 1998) “AI . . . is concerned with intelligent behavior in artifacts.” (Nilsson, 1998)

10. PEAS P erformance Measure: Performance measure is the unit to define the success of an agent. Performance varies with agents based on their different precepts. E nvironment: Environment is the surrounding of an agent at every instant. It keeps changing with time if the agent is set in motion. There are 5 major types of environments: Fully Observable & Partially Observable Episodic & Sequential Static & Dynamic Discrete & Continuous Deterministic & Stochastic A ctuator: An actuator is a part of the agent that delivers the output of action to the environment. S ensor: Sensors are the receptive parts of an agent that takes in the input for the agent.

11. Deterministic and non-deterministic An environment is deterministic if the next state of the environment is solely determined by the current state of the environment and the actions selected by the agents . An inaccessible environment might appear to be non-deterministic since the agent has no way of sensing part of the environment and the result of its actions on it. We have to take into consideration the point of view of the agent when determining whether an environment is deterministic or not since the agent might have limited perception capabilities.   Episodic and non-episodic In an episodic environment, the agent's experience is divided into episodes which consist of a percept sequence and an action. Since episodes are independent of one another and the agent doesn't need to know the effect of its actions.   Static and dynamic An environment is dynamic if it changes while an agent is in the process of responding to a percept sequence. It is static if it does not change while the agent is deciding on an action i.e the agent does not to keep in touch with time. An environment is semidynamic if it does not change with timebut he agent's performace score does.   Discrete and continuous If the number of percepts and actions in the environment is limited and distinct then the environment is said to be discrete.eg A chess board

12. Agent Performance Measure Environment Actuator Sensor Hospital Management System Patient’s health, Admission process, Payment Hospital, Doctors, Patients Prescription, Diagnosis, Scan report Symptoms, Patient’s response Automated Car Drive The comfortable trip, Safety, Maximum Distance Roads, Traffic, Vehicles Steering wheel, Accelerator, Brake, Mirror Camera, GPS, Odometer Subject Tutoring Maximize scores, Improvement is students Classroom, Desk, Chair, Board, Staff, Students Smart displays, Corrections Eyes, Ears, Notebooks Part-picking robot Percentage of parts in correct bins Conveyor belt with parts; bins Jointed arms and hand Camera, joint angle sensors Satellite image analysis system Correct image categorization Downlink from orbiting satellite Display categorization of scene Color pixel arrays AI rational agent examples

13. AI TECHNIQUES AI deals with practical problems, identification and authentication problem, interdependent and cross-domain problems, and classification problems. Need for AI techniques: Analysis of voluminous and large amount of data from multi-domain Characterization of miscellaneous data and mapping of this data with reference to built-in knowledge and building the knowledge further Dealing with the constantly changing scenarios and situations Data Analytics [ Collecting till decision making] Knowledge building based on limited relevant data from huge pool of irrelevant data The main objective of AI techniques is to capture knowledge based on data and information. AI techniques need to handle different problems that can be categorized as following: Structured problems : Defined goal state Unstructured problems : Goal state not known Linear problems : Based on dependent variables (Linear classification) Non linear problems : No dependency between variables

14. Problem Solving with AI Well structured problems A well structured problems yield a right answer or right interference when appropriate algorithm is applied. Examples: 1. Solving quadratic equation 2. Calculating speed of ball when it reached the batsman Ill structured problems An ill-structured problem do not yield a particular answer. Examples: (Real world problems) Challenging due to lack of defined steps and lack of well defined criterion to evaluate the outcome

15. AI Models Dunker introduced ‘maze hypothesis ’, where the creative and intelligent tasks are modelled like a set of maze of paths from an initial node to a certain or resultant node. All problems cannot be solved using maze-approach, which lead to Logic Theory Machines. Applied to general problem solving like Chess, where there is controlled environment with given situation and goal. Semiotic models: Based on sign process, signification or communication . Eg : Associating Thumbs-up gesture with positivity. Statistical models: Representation and formalization of relationships through statistical techniques. Uses probabilistic approaches

16. Data Acquisition and Learning Aspects in AI Knowledge Discovery – Data Mining and Machine Learning Information : Pattern underlying data Data : Recorded facts Data mining and Knowledge discovery : Extraction of meaningful information Data mining: Data cleaning, preprocessing, identifying and interpreting the patterns, understanding the applications and generating the target data with consolidated patterns. Machine Learning : Making machine intelligible based on past experience . Computational Learning Theory (COLT) Formal mathematical models defined to analyze the efficiency and complexity in terms of computation, prediction and feasibility of algorithm. Applied in machine learning, pattern recognition, statistics and so on.

17. Neural and Evolutionary Computation Evolutionary Computation enabled to speed up data mining . Neural computing involves stimulating the neural behavior of human to enable machine to learn . Artificial Neural Network (ANN) is configured for applications like pattern recognition or classification. Intelligent agent and multi-agent systems Agent: Software program Intelligent agent : flexible in terms of actions to get desired outcomes. It is goal directed, reacts with environment and acts accordingly. Complex tasks and decision making demand combination of more than one percept where group of intelligent agents required to solve the problem - Multi agent System (MAS) Multi-perspective integrated intelligence Utilizing and exploiting knowledge form different perspective to build an intelligent system. Information collected from different perspectives is used for final decision-making. The collected information can be continuous or discrete.

18. Problem solving in AI Problem solving-process of generating solutions for the given situation Problem is defined, in a context has well defined objective solution has set of activities Uses previous knowledge and domain knowledge Primary objective-problem identification

19. Types of problem solving General purpose: Means-ends analysis present situation is compared with the goal to detect the difference select action that reduces the difference Ex: select the mode of transport Special purpose-modelled for the specific problem, which have specific features Ex: classify legal document reference to particular case Problem solving technique involves problem definition - problem formulation problem analysis and representation planning execution evaluating solution consolidating gains - Goals Problem solving in AI Goal formulation Search and execute

20. Formulating Problems Well-defined problems and solutions - A problem is really a collection of information that the agent will use to decide what to do . Elements of a problem: 1. The initial state that the agent knows itself to be in. 2. The set of possible actions available to the agent. operator is used to denote the description of an action to reach a state. state space-the set of all states reachable from the initial state by any sequence of actions. A path in the state space is simply any sequence of actions leading from one state to another . 3. The goal test , which the agent can apply to a single state description to determine if it is a goal state. 4. A path cost function is a function that assigns a cost to a path. 5. The output of a search algorithm is a solution , that is, a path from the initial state to a state that satisfies the goal test.

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

22. Problem: To reach from initial state to final state with minimum number of moves Illustration Algorithm A well defined problem is described in terms of

23. PROBLEM TYPES AND CHARACTERISTICS Problem types Single-state problem / Deterministic or Observable Multiple-state problem/ Non observable Contingency problem/Non-deterministic or partially observable Exploration problem /Unknown state space Problem Characteristics : To choose an appropriate method for a particular Problem: Is the problem decomposable? Can solution steps be ignored or undone? Is the universe predictable? Is a good solution absolute or relative? Is the solution a state or a path? What is the role of knowledge? Does the task require human‐interaction?

24. Is the problem decomposable into small sub-problems which are easy to solve? Can the problem be broken down into smaller problems to be solved independently? The decomposable problem can be solved easily. Can solution steps be ignored or undone? In the Theorem Proving problem, a lemma that has been proved can be ignored for the next steps. Such problems are called Ignorable problems. In the 8-Puzzle, Moves can be undone and backtracked. Such problems are called Recoverable problems. In Playing Chess, moves can be retracted (withdraw). Such problems are called Irrecoverable problems. Ignorable problems can be solved using a simple control structure that never backtracks. Recoverable problems can be solved using backtracking. Irrecoverable problems can be solved by recoverable style methods via planning.

25. Is the universe of the problem is predictable? In Playing Bridge, We cannot know exactly where all the cards are or what the other players will do on their turns. Uncertain outcome! For certain-outcome problems , planning can be used to generate a sequence of operators that is guaranteed to lead to a solution. For uncertain-outcome problems , a sequence of generated operators can only have a good probability of leading to a solution. Plan revision is made as the plan is carried out and the necessary feedback is provided. Is a good solution to the problem is absolute or relative? The Travelling Salesman Problem, we have to try all paths to find the shortest one. Any path problem can be solved using heuristics that suggest good paths to explore. For best-path problems, a much more exhaustive search will be performed.

26. Is the solution to the problem a state or a path 28 The Water Jug Problem, the path that leads to the goal must be reported. A path-solution problem can be reformulated as a state-solution problem by describing a state as a partial path to a solution. The question is whether that is natural or not .

27. Does the task of solving a problem require human interaction? 30 The solitary problem , in which there is no intermediate communication and no demand for an explanation of the reasoning process. The conversational problem , in which intermediate communication is to provide either additional assistance to the computer or additional information to the user. Role of knowledge

28. SEARCH Search is a general algorithm that helps in finding the path in state space The path may lead to the solution or dead end. Forward search(data directed) Starts search from initial state towards goal state. Ex: locating a city from current location Backward search(goal directed) Search stars from goal state towards a solvable initial state. Ex: start from target city

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