ELE 4061: ARTIFICIAL INTELLIGENCE [3 0 0 3] 31/01/2023 ELE 4061: ARTIFICIAL INTELLIGENCE [3 0 0 3] Foundation and History of AI, State of the art, Fields of application, Performance measures, Rationality, Specification and properties of task environment, Structure of Agents, Problem solving by searching, Searching for solutions, uninformed search strategies, Informed search strategies, Heuristic functions, Local search algorithms, Online search agents, Knowledge based agents, The Wumpus World, Propositional logic – reasoning patterns, effective inference, First order logic – Syntax and semantics, Knowledge engineering, Inference rule, forward and backward chaining, Ontological engineering, categories and objects, Processes and intervals, reasoning systems, Truth maintenance systems, Uncertainty, Basic probability notation, Axioms, Baye's rule, Bayesian networks, Inference in Bayesian networks. References: 1. Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (3e), Pearson, 2012. 2. Elaine Rich, Kevin Knight and Shivashankar B. Nair, Artificial Intelligence (3e), Tata McGraw Hill, 2012. 3. David Poole and Alan Mackworth, Artificial Intelligence: Foundations of Computational Agents (2e), Cambridge University Press, 2017. ELE 4061: ARTIFICIAL INTELLIGENCE [3 0 0 3] Course learning outcomes: CO1: Understand the concept of Artificial Intelligence and its effect on prevalent technologies. CO2: Evaluate the role of agents and how it is related to various environments CO3: Apply and analyze different search techniques on various applications. CO4: Interpret various real-world Knowledge representation techniques CO5: Analyze and design a solution for a real-world problem by understanding the dynamic behavior of the system Introduction to Artificial Intelligence: Foundations of Artificial Intelligence, History of Artificial Intelligence, The state of the Art, Real-time examples. [4] Intelligent Agents: Agents and Environments, The concept of Rationality, The Nature of Environments, The structure of Agents, Examples. [4] Solving problems by Searching: Problem Solving agents, Searching for Solutions, Uninformed search strategies, Informed (Heuristic) search strategies, Heuristic functions, local search algorithms and optimization problems, local search in continuous spaces, searching with non-deterministic actions, searching with partial observations, online search agents and unknown environments. [6] Adversarial Search and Constraint satisfaction Problems: Games, Optimal decision in games, Alpha-Beta Pruning, imperfect real-time decisions, stochastic games, Defining constraint satisfaction problems, constraint propagation, backtracking search and local search for CSPs, Examples [6] Knowledge and Reasoning: Knowledge-based agents, The wumpus world, logic, propositional logic, firstorder logic, syntax and semantics, knowledge engineering and inference in first-order logic [4] Knowledge representation: Ontological Engineering, Categories and objects, Reasoning systems for categories [4] Uncertain knowledge and Reasoning: Acting under uncertainty, Basic probability notation, Bayes' rule, representing knowledge in uncertainties, semantics of Bayesian networks, Examples [4] References: 1. Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (3e), Pearson, 2012. 2. Elaine Rich, Kevin Knight and Shivashankar B. Nair, Artificial Intelligence (3e), Tata McGraw Hill, 2012. 3. David Poole and Alan Mackworth, Artificial Intelligence: Foundations of Computational Agents (2e), Cambridge University Press, 2017. Foundation and History of AI, State of the art, Fields of application AI can be defined as an approach of creating a system, or a software, having intelligence same as humans and perform task with the same efficiency as human do. • AI has done some amazing changes in the world, which we didn’t even think of. • Intelligent systems talking to us or offering services in every day life. • Machine as “having its own intelligence,” Intelligence History of AI can be explained mainly in six groups 1. Beginning: 1943–1952 2. 1952–1969: Early enthusiasm, high hopes – ELIZA chatbot 3. 1952–1969: Sobering up 4. 1970–1979: Knowledge-based systems 5. 1980–2010: AI becomes an industry – the era of Intelligent Agents, Machine Learning, Robotics 6. 2010–till date: the era of Deep Learning Branches or sub-area of AI Trends in AI There is a large array of applications where AI is serving common people in their day-to-day lives which can be grouped into the following categories. Applications in the areas of…(not limited): • Marketing • Game playing • Banking • Mathematics • Finance • Agriculture • Healthcare • Gaming • Autonomous control • Diagnosis • Logistics planning • Autonomous planning and scheduling • Space exploration • Language understanding and problem solving • Autonomous vehicles • Robotics • Chatbots • Natural Language Generation • Speech recognition AI in marketing AI in banking AI in finance Ai in agriculture AI in health care AI in gaming Components of AI In AI, the intelligence is intangible which is composed of mainly five techniques as follows Reasoning Learning Problem solving Perception Linguistic intelligence Components of AI Reasoning- Reasoning is the set of processes that enables an intelligent system to help or provide basis for actions, making decisions, and prediction. Reasoning is of two types: Inductive Reasoning and Deductive Reasoning. Learning- Learning is the process of gaining knowledge by understanding, practicing, being taught, or experiencing one thing. Learning enhances the awareness of any topic. Problem solving- Problem solving is the method during which one perceives and tries to make a desired answer from a present state of affairs by taking some path, that is blocked by known or unknown hurdles. Components of AI 4. Perception- Perception is the method of decoding, selecting, and organizing sensory data. presumes sensing. Within the domain of AI, mechanism puts the info acquired by the sensors very meaningful manner. acquiring, Perception perception along in a 5. Linguistic intelligence- Linguistic intelligence is one’s ability to use, comprehend, speak, and write the verbal and written language. It is important in interpersonal communication. One name “Artificial Intelligence” for many trends. AI Programming languages A number of programming languages exist that are used to build AI systems. General programming languages, such as C++, R, Java, Python, and LISP (List Processing) are frequently used. Here are some languages that are most typically used for creating the AI projects: PROLOG LISP R Python Java C+ Future of AI: Robots With AI Automated Detection and Prevention AI in Cloud AI and IOT vijaya.laxmi@manipal.edu 9448547164 AB1, Ground Floor Analog system Design Lab
