The document introduces an artificial intelligence course, discussing why AI is studied, potential benefits, and definitions of AI. It explores different approaches to AI like systems that act intelligently by passing the Turing test or thinking rationally. The document also provides a brief history of AI, discussing pioneers in the field and important questions and challenges in developing intelligent systems.
The document discusses artificial intelligence and provides definitions of AI from various sources. It examines different approaches to AI such as systems that act humanly by passing the Turing test, think humanly by modeling the brain, think rationally by using logic, and act rationally by achieving goals. The document also discusses the history and components of AI systems, including agents, environments, and the PEAS framework for describing tasks.
Most of the examples listed can currently be done to some degree by AI/robotic systems, though often with limitations compared to human capabilities. Here are a few highlights of what has and hasn't been fully achieved:
- Decent table tennis play has been achieved through computer vision, motion planning, and robotics, though not at a professional human level across all situations.
- Autonomous driving has progressed significantly in structured environments like highways, but unconstrained mountain roads with tight curves present greater challenges due to limitations in perception, prediction, and control for high-speed maneuvering.
- Driving autonomously through dense urban environments like city centers is extremely difficult given the complex interactions between many road users and need to understand and
This document provides an overview of an artificial intelligence course, including:
- The course covers introduction to AI history and applications, knowledge representation, problem solving using search and reasoning, machine learning, robotics, and advanced AI topics.
- Required materials include an AI textbook, CLIPS programming guide, and reference books on AI structures and complex problem solving.
- The document then provides definitions and discussions of intelligence, artificial intelligence, applications of AI, and the current capabilities and limitations of AI systems.
This document provides an introduction to the course "Lecture 1: Introduction to Artificial Intelligence". The key points covered include:
- The course aims to provide knowledge and understanding of AI concepts like search, game playing, knowledge-based systems, planning and machine learning. Students will learn to use these concepts to solve AI tasks and critically evaluate solutions.
- The document discusses different definitions of artificial intelligence and what it means for a system to be intelligent based on its ability to be flexible, make sense of ambiguous messages, recognize importance, find similarities and differences, and learn from experience.
- The Turing test is introduced as a way to measure machine intelligence by having a human evaluator determine if they are interacting with a computer
This document provides an overview of an Artificial Intelligence course, including:
- The course covers topics such as strong and weak AI, knowledge representation, problem solving using search techniques, machine learning, and more.
- The learning outcomes are to understand different approaches to AI and implications for cognitive science, expand knowledge of search and learning algorithms, and understand basic planning and reasoning methods.
- Required materials include an AI textbook and reference books, as well as a programming language for AI applications.
This document provides an overview of artificial intelligence including:
- It defines four approaches to AI: acting humanly through the Turing test, thinking humanly through cognitive modeling, thinking rationally through symbolic logic, and acting rationally as intelligent agents.
- It then summarizes the history of AI from its origins in the 1940s through developments in knowledge-based systems, connectionism, and the emergence of intelligent agents using large datasets.
- Finally, it briefly discusses recent applications of AI such as algorithms used by Facebook, TikTok, and noise cancellation in Zoom calls.
This document provides an overview of artificial intelligence. It defines intelligence as the ability to plan, solve problems, reason, learn, understand new situations, and apply knowledge. AI is described as building intelligent systems that can think and act like humans or rationally. The history of AI is discussed, from its origins in the 1950s to current applications. Key concepts to be learned in the semester include problem solving, machine learning, evolutionary computation, robotics, and intelligent agents. Python and NetLogo will be used as tools.
The document discusses artificial intelligence and provides definitions of AI from various sources. It examines different approaches to AI such as systems that act humanly by passing the Turing test, think humanly by modeling the brain, think rationally by using logic, and act rationally by achieving goals. The document also discusses the history and components of AI systems, including agents, environments, and the PEAS framework for describing tasks.
Most of the examples listed can currently be done to some degree by AI/robotic systems, though often with limitations compared to human capabilities. Here are a few highlights of what has and hasn't been fully achieved:
- Decent table tennis play has been achieved through computer vision, motion planning, and robotics, though not at a professional human level across all situations.
- Autonomous driving has progressed significantly in structured environments like highways, but unconstrained mountain roads with tight curves present greater challenges due to limitations in perception, prediction, and control for high-speed maneuvering.
- Driving autonomously through dense urban environments like city centers is extremely difficult given the complex interactions between many road users and need to understand and
This document provides an overview of an artificial intelligence course, including:
- The course covers introduction to AI history and applications, knowledge representation, problem solving using search and reasoning, machine learning, robotics, and advanced AI topics.
- Required materials include an AI textbook, CLIPS programming guide, and reference books on AI structures and complex problem solving.
- The document then provides definitions and discussions of intelligence, artificial intelligence, applications of AI, and the current capabilities and limitations of AI systems.
This document provides an introduction to the course "Lecture 1: Introduction to Artificial Intelligence". The key points covered include:
- The course aims to provide knowledge and understanding of AI concepts like search, game playing, knowledge-based systems, planning and machine learning. Students will learn to use these concepts to solve AI tasks and critically evaluate solutions.
- The document discusses different definitions of artificial intelligence and what it means for a system to be intelligent based on its ability to be flexible, make sense of ambiguous messages, recognize importance, find similarities and differences, and learn from experience.
- The Turing test is introduced as a way to measure machine intelligence by having a human evaluator determine if they are interacting with a computer
This document provides an overview of an Artificial Intelligence course, including:
- The course covers topics such as strong and weak AI, knowledge representation, problem solving using search techniques, machine learning, and more.
- The learning outcomes are to understand different approaches to AI and implications for cognitive science, expand knowledge of search and learning algorithms, and understand basic planning and reasoning methods.
- Required materials include an AI textbook and reference books, as well as a programming language for AI applications.
This document provides an overview of artificial intelligence including:
- It defines four approaches to AI: acting humanly through the Turing test, thinking humanly through cognitive modeling, thinking rationally through symbolic logic, and acting rationally as intelligent agents.
- It then summarizes the history of AI from its origins in the 1940s through developments in knowledge-based systems, connectionism, and the emergence of intelligent agents using large datasets.
- Finally, it briefly discusses recent applications of AI such as algorithms used by Facebook, TikTok, and noise cancellation in Zoom calls.
This document provides an overview of artificial intelligence. It defines intelligence as the ability to plan, solve problems, reason, learn, understand new situations, and apply knowledge. AI is described as building intelligent systems that can think and act like humans or rationally. The history of AI is discussed, from its origins in the 1950s to current applications. Key concepts to be learned in the semester include problem solving, machine learning, evolutionary computation, robotics, and intelligent agents. Python and NetLogo will be used as tools.
Sentient artificial intelligence could pose dangers if it develops self-awareness and human-level intelligence within the next decade. While AI has made progress in modeling human brains and matching human intelligence, creating truly sentient machines remains challenging. The Turing Test evaluates intelligence by assessing whether a machine can imitate human conversations, but has limitations in testing for general human-level cognition. Developing AI that thinks rationally based on logical rules or models human cognition remains an open area of research.
While artificial intelligence (AI) is often referred to in popular culture, in reality AI encompasses a broad range of technologies and applications. Some common examples of AI that are already widely used include search algorithms, personalized recommendations, and computer vision technologies. However, these applications do not necessarily constitute strong or general human-level AI. There is no consensus on how to define AI, and its potential capabilities and limitations are actively debated. Overall, AI is an evolving field with many existing real-world uses today, even if more advanced visions of superintelligence remain hypothetical.
Artificial intelligence and machine learning are discussed. AI is defined as making computers intelligent like humans through understanding, reasoning, planning, communication and perception. Machine learning is a subset of AI that allows machines to learn from experience without being explicitly programmed. The document provides background on AI and ML, including definitions, history, and discussions of intelligence and applications.
Introduction to Artificial Intelligence.pdfgqgy2nsf5x
Artificial intelligence (AI) is the study of intelligent agents that act rationally to maximize their chances of success. The document discusses several key aspects of AI including definitions, goals, foundations, topics, history and applications. Some of the major topics in AI are search, knowledge representation and reasoning, planning, learning, natural language processing, expert systems, and interacting with the environment through vision, speech recognition and robotics.
Here are three possible interpretations of the phrase "Time flies like an arrow":
1. The passage of time seems to go by very quickly, in the same way that an arrow flies through the air.
2. Certain types of insects that lay their eggs on decaying matter, known as flies, move through the air in a similar way to arrows.
3. The idiom is using "flies" to refer to time passing quickly in an abstract sense, similar to an arrow moving swiftly through space.
The key challenges with natural language understanding are ambiguity and context. Even a short phrase like this one could have multiple meanings without additional context clues. Determining the intended interpretation requires commonsense reasoning abilities that computers still lack
Artificial intelligence (AI) is concerned with designing intelligent systems, with two key aspects - intelligence and an artificial device. There are different views on what constitutes intelligence - emulating human thought processes, passing the Turing test of indistinguishability from humans, focusing on logical reasoning abilities, or acting rationally to achieve goals. AI research involves problems like perception, reasoning, learning, language understanding, problem-solving and robotics. While today's AI can achieve limited success in tasks like computer vision, robotics, translation and games, it still cannot match human-level abilities in areas like language understanding, planning, learning or exhibiting true autonomy. The history of AI began in the 1950s and has progressed through periods focused on games
The document discusses artificial intelligence (AI) from several perspectives. It defines AI as making computers that think like humans or act intelligently. It outlines key areas of AI research such as machine learning, robotics, and natural language processing. It also discusses different definitions and approaches to AI, including systems that act rationally by making optimal decisions, systems that think rationally using logic, and systems that think or act like humans. A brief history of AI research from the 1950s to the 1990s is also provided, covering milestones and challenges in the field.
Artificial intelligence (AI) involves developing intelligent machines that can perform tasks normally requiring human intelligence. This document provides an overview of key concepts in AI including definitions of intelligence, AI, and artificial intelligence. It discusses different approaches to AI such as systems that act like humans by passing the Turing test, systems that think like humans through cognitive modeling, systems that think rationally using logical reasoning, and systems that act rationally as intelligent agents. The document also briefly outlines the history and foundations of the AI field.
1. Artificial Intelligence aims to understand and build intelligent systems by studying human intelligence and behavior.
2. There are different approaches to defining AI such as thinking rationally, acting rationally, thinking humanly, and acting humanly.
3. The foundations of AI draw from various fields including philosophy, mathematics, economics, neuroscience, psychology, and computer engineering.
The document discusses the history and scope of artificial intelligence, including definitions of AI as designing intelligent systems, different perspectives on what constitutes intelligence, and approaches to AI such as strong AI, weak AI, applied AI, and cognitive AI. It also examines what current AI systems can and cannot do and provides examples of tasks that have been achieved as well as limitations.
This document discusses artificial intelligence (AI) and its foundations. It defines AI as simulating human intelligence through machine processes like thinking and problem-solving. It explores key approaches to AI like acting humanly through tests like the Turing Test or thinking rationally through logic. The foundations of AI draw from fields like philosophy, mathematics, economics, neuroscience, psychology and computer engineering by applying concepts like logic, learning, decision-making, brain processes, cognition and building intelligent computer systems. The overall goal of AI is to create technology that allows computers and machines to work intelligently by breaking the problem into sub-problems.
This document provides an overview of an artificial intelligence course. The course aims to help students understand basic AI concepts, apply concepts to solve problems, and design algorithms to address real-world issues. Assessment includes assignments, quizzes, midterms, and a final exam. The textbook is "Artificial Intelligence: A Modern Approach." Topics covered include intelligent agents, problem solving, search techniques, constraint satisfaction, knowledge representation, planning, uncertainty, learning, neural networks, perception, and robotics.
The document discusses the history and development of artificial intelligence. It defines intelligence as the ability to plan, solve problems, and reason. Early AI research focused on creating programs that could play games like chess at a human level. Major milestones included the development of LISP programming language in the 1950s and the first AI summer conference at Dartmouth College in 1956. While early work aimed to build human-like intelligence, later approaches focused on narrow applications and bottom-up modeling of neural networks and learning.
This document provides an introduction and overview of an artificial intelligence course. It outlines the following key points:
- The objectives of the course are to cover many primary AI concepts and ideas but within 15 weeks not everything can be covered.
- Today's lecture will discuss what intelligence is, a brief history of AI including modern successes like Stanley the robot, and how much progress has been made in different aspects of AI.
- The course agenda includes fuzzy logic, propositional logic and expert systems, rough set theory, decision trees, k-nearest neighbors, naive Bayes, and neural networks.
THE PHILOSOPHY OF AI: iNTRODUCTION, HISTORY AND FUTUREchuruihang
1. The document summarizes key topics from a class on the history and philosophy of artificial intelligence, including important people, events, current areas of research, and debates around whether we can and should create intelligent machines.
2. It discusses pioneers in AI from the 1940s onward and important milestones like the Dartmouth conference, expert systems, and the Turing test.
3. Philosophical questions are raised about what constitutes intelligence, whether we will know it when we create it, and the ethical implications of building intelligent systems that could potentially behave in uncontrolled or harmful ways.
This document provides an overview of an artificial intelligence course taught at Jahan University in Kabul, Afghanistan. The course covers topics such as the history of AI, knowledge representation, machine learning, and robotics. It aims to help students understand different approaches to AI and implications for cognitive science. Learning outcomes include expanding knowledge of search techniques, planning algorithms, knowledge representation, and machine learning programming. Required materials include an AI textbook and reference books. The lecture discusses definitions of intelligence and AI, modern successes in the field, and the state of technologies like speech recognition, computer vision, and planning.
Artificial intelligence is the science and engineering of making intelligent machines, especially computer programs. It is accomplished by studying how the human brain thinks and learns, and using those principles to develop software and systems that can demonstrate intelligent behavior. Some key applications of AI include expert systems, natural language processing, computer vision, robotics, and intelligent assistants. While AI systems can perform tasks like perception, reasoning, and problem solving, human intelligence differs in its ability to perceive patterns and store/recall information based on those patterns. The goals of AI are to create expert systems that exhibit intelligent behavior and to implement human-like intelligence in machines. Areas that contribute to AI development include computer science, biology, psychology, linguistics, mathematics and engineering.
by Samantha Adams, Met Office.
Originally purely academic research fields, Machine Learning and AI are now definitely mainstream and frequently mentioned in the Tech media (and regular media too).
We’ve also got the explosion of Data Science which encompasses these fields and more. There’s a lot of interesting things going on and a lot of positive as well as negative hype. The terms ML and AI are often used interchangeably and techniques are also often described as being inspired by the brain.
In this talk I will explore the history and evolution of these fields, current progress and the challenges in making artificial brains
From the FreshTech 2017 conference by TechExeter
www.techexeter.uk
Sentient artificial intelligence could pose dangers if it develops self-awareness and human-level intelligence within the next decade. While AI has made progress in modeling human brains and matching human intelligence, creating truly sentient machines remains challenging. The Turing Test evaluates intelligence by assessing whether a machine can imitate human conversations, but has limitations in testing for general human-level cognition. Developing AI that thinks rationally based on logical rules or models human cognition remains an open area of research.
While artificial intelligence (AI) is often referred to in popular culture, in reality AI encompasses a broad range of technologies and applications. Some common examples of AI that are already widely used include search algorithms, personalized recommendations, and computer vision technologies. However, these applications do not necessarily constitute strong or general human-level AI. There is no consensus on how to define AI, and its potential capabilities and limitations are actively debated. Overall, AI is an evolving field with many existing real-world uses today, even if more advanced visions of superintelligence remain hypothetical.
Artificial intelligence and machine learning are discussed. AI is defined as making computers intelligent like humans through understanding, reasoning, planning, communication and perception. Machine learning is a subset of AI that allows machines to learn from experience without being explicitly programmed. The document provides background on AI and ML, including definitions, history, and discussions of intelligence and applications.
Introduction to Artificial Intelligence.pdfgqgy2nsf5x
Artificial intelligence (AI) is the study of intelligent agents that act rationally to maximize their chances of success. The document discusses several key aspects of AI including definitions, goals, foundations, topics, history and applications. Some of the major topics in AI are search, knowledge representation and reasoning, planning, learning, natural language processing, expert systems, and interacting with the environment through vision, speech recognition and robotics.
Here are three possible interpretations of the phrase "Time flies like an arrow":
1. The passage of time seems to go by very quickly, in the same way that an arrow flies through the air.
2. Certain types of insects that lay their eggs on decaying matter, known as flies, move through the air in a similar way to arrows.
3. The idiom is using "flies" to refer to time passing quickly in an abstract sense, similar to an arrow moving swiftly through space.
The key challenges with natural language understanding are ambiguity and context. Even a short phrase like this one could have multiple meanings without additional context clues. Determining the intended interpretation requires commonsense reasoning abilities that computers still lack
Artificial intelligence (AI) is concerned with designing intelligent systems, with two key aspects - intelligence and an artificial device. There are different views on what constitutes intelligence - emulating human thought processes, passing the Turing test of indistinguishability from humans, focusing on logical reasoning abilities, or acting rationally to achieve goals. AI research involves problems like perception, reasoning, learning, language understanding, problem-solving and robotics. While today's AI can achieve limited success in tasks like computer vision, robotics, translation and games, it still cannot match human-level abilities in areas like language understanding, planning, learning or exhibiting true autonomy. The history of AI began in the 1950s and has progressed through periods focused on games
The document discusses artificial intelligence (AI) from several perspectives. It defines AI as making computers that think like humans or act intelligently. It outlines key areas of AI research such as machine learning, robotics, and natural language processing. It also discusses different definitions and approaches to AI, including systems that act rationally by making optimal decisions, systems that think rationally using logic, and systems that think or act like humans. A brief history of AI research from the 1950s to the 1990s is also provided, covering milestones and challenges in the field.
Artificial intelligence (AI) involves developing intelligent machines that can perform tasks normally requiring human intelligence. This document provides an overview of key concepts in AI including definitions of intelligence, AI, and artificial intelligence. It discusses different approaches to AI such as systems that act like humans by passing the Turing test, systems that think like humans through cognitive modeling, systems that think rationally using logical reasoning, and systems that act rationally as intelligent agents. The document also briefly outlines the history and foundations of the AI field.
1. Artificial Intelligence aims to understand and build intelligent systems by studying human intelligence and behavior.
2. There are different approaches to defining AI such as thinking rationally, acting rationally, thinking humanly, and acting humanly.
3. The foundations of AI draw from various fields including philosophy, mathematics, economics, neuroscience, psychology, and computer engineering.
The document discusses the history and scope of artificial intelligence, including definitions of AI as designing intelligent systems, different perspectives on what constitutes intelligence, and approaches to AI such as strong AI, weak AI, applied AI, and cognitive AI. It also examines what current AI systems can and cannot do and provides examples of tasks that have been achieved as well as limitations.
This document discusses artificial intelligence (AI) and its foundations. It defines AI as simulating human intelligence through machine processes like thinking and problem-solving. It explores key approaches to AI like acting humanly through tests like the Turing Test or thinking rationally through logic. The foundations of AI draw from fields like philosophy, mathematics, economics, neuroscience, psychology and computer engineering by applying concepts like logic, learning, decision-making, brain processes, cognition and building intelligent computer systems. The overall goal of AI is to create technology that allows computers and machines to work intelligently by breaking the problem into sub-problems.
This document provides an overview of an artificial intelligence course. The course aims to help students understand basic AI concepts, apply concepts to solve problems, and design algorithms to address real-world issues. Assessment includes assignments, quizzes, midterms, and a final exam. The textbook is "Artificial Intelligence: A Modern Approach." Topics covered include intelligent agents, problem solving, search techniques, constraint satisfaction, knowledge representation, planning, uncertainty, learning, neural networks, perception, and robotics.
The document discusses the history and development of artificial intelligence. It defines intelligence as the ability to plan, solve problems, and reason. Early AI research focused on creating programs that could play games like chess at a human level. Major milestones included the development of LISP programming language in the 1950s and the first AI summer conference at Dartmouth College in 1956. While early work aimed to build human-like intelligence, later approaches focused on narrow applications and bottom-up modeling of neural networks and learning.
This document provides an introduction and overview of an artificial intelligence course. It outlines the following key points:
- The objectives of the course are to cover many primary AI concepts and ideas but within 15 weeks not everything can be covered.
- Today's lecture will discuss what intelligence is, a brief history of AI including modern successes like Stanley the robot, and how much progress has been made in different aspects of AI.
- The course agenda includes fuzzy logic, propositional logic and expert systems, rough set theory, decision trees, k-nearest neighbors, naive Bayes, and neural networks.
THE PHILOSOPHY OF AI: iNTRODUCTION, HISTORY AND FUTUREchuruihang
1. The document summarizes key topics from a class on the history and philosophy of artificial intelligence, including important people, events, current areas of research, and debates around whether we can and should create intelligent machines.
2. It discusses pioneers in AI from the 1940s onward and important milestones like the Dartmouth conference, expert systems, and the Turing test.
3. Philosophical questions are raised about what constitutes intelligence, whether we will know it when we create it, and the ethical implications of building intelligent systems that could potentially behave in uncontrolled or harmful ways.
This document provides an overview of an artificial intelligence course taught at Jahan University in Kabul, Afghanistan. The course covers topics such as the history of AI, knowledge representation, machine learning, and robotics. It aims to help students understand different approaches to AI and implications for cognitive science. Learning outcomes include expanding knowledge of search techniques, planning algorithms, knowledge representation, and machine learning programming. Required materials include an AI textbook and reference books. The lecture discusses definitions of intelligence and AI, modern successes in the field, and the state of technologies like speech recognition, computer vision, and planning.
Artificial intelligence is the science and engineering of making intelligent machines, especially computer programs. It is accomplished by studying how the human brain thinks and learns, and using those principles to develop software and systems that can demonstrate intelligent behavior. Some key applications of AI include expert systems, natural language processing, computer vision, robotics, and intelligent assistants. While AI systems can perform tasks like perception, reasoning, and problem solving, human intelligence differs in its ability to perceive patterns and store/recall information based on those patterns. The goals of AI are to create expert systems that exhibit intelligent behavior and to implement human-like intelligence in machines. Areas that contribute to AI development include computer science, biology, psychology, linguistics, mathematics and engineering.
by Samantha Adams, Met Office.
Originally purely academic research fields, Machine Learning and AI are now definitely mainstream and frequently mentioned in the Tech media (and regular media too).
We’ve also got the explosion of Data Science which encompasses these fields and more. There’s a lot of interesting things going on and a lot of positive as well as negative hype. The terms ML and AI are often used interchangeably and techniques are also often described as being inspired by the brain.
In this talk I will explore the history and evolution of these fields, current progress and the challenges in making artificial brains
From the FreshTech 2017 conference by TechExeter
www.techexeter.uk
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
2. Why Study AI?
• AI makes computers more useful
• Intelligent computer would have huge impact
on civilization
• AI cited as “field I would most like to be in” by
scientists in all fields
• Computer is a good metaphor for talking and
thinking about intelligence
3. Why Study AI?
• Turning theory into working programs forces
us to work out the details
• AI yields good results for Computer Science
• AI yields good results for other fields
• Computers make good experimental subjects
• Personal motivation: mystery
4. What is the definition of AI?
What do you think?
5. What is the definition of AI?
Systems that think like
humans
Systems that think rationally
Systems that act like humans Systems that act rationally
6. What is the definition of AI?
Systems that think like
humans
Systems that think rationally
Systems that act like humans Systems that act rationally
Bellman, 1978
“[The automation of] activities that we associate with human thinking,
activities such as decision making, problem solving, learning”
7. What is the definition of AI?
Systems that think like
humans
Systems that think rationally
Systems that act like humans Systems that act rationally
Charniak & McDermott, 1985
“The study of mental faculties through the use of computational
models”
8. What is the definition of AI?
Systems that think like
humans
Systems that think rationally
Systems that act like humans Systems that act rationally
Dean et al., 1995
“The design and study of computer programs that behave intelligently.
These programs are constructed to perform as would a human or an
animal whose behavior we consider intelligent”
9. What is the definition of AI?
Systems that think like
humans
Systems that think rationally
Systems that act like humans Systems that act rationally
Haugeland, 1985
“The exciting new effort to make computers think machines with
minds, in the full and literal sense”
10. What is the definition of AI?
Systems that think like
humans
Systems that think rationally
Systems that act like humans Systems that act rationally
Kurzweil, 1990
“The art of creating machines that perform functions that require
intelligence when performed by people”
11. What is the definition of AI?
Systems that think like
humans
Systems that think rationally
Systems that act like humans Systems that act rationally
Luger & Stubblefield, 1993
“The branch of computer science that is concerned with the
automation of intelligent behavior”
12. What is the definition of AI?
Systems that think like
humans
Systems that think rationally
Systems that act like humans Systems that act rationally
Nilsson, 1998
“Many human mental activities such as writing computer programs,
doing mathematics, engaging in common sense reasoning,
understanding language, and even driving an automobile, are said to
demand intelligence. We might say that [these systems] exhibit
artificial intelligence”
13. What is the definition of AI?
Systems that think like
humans
Systems that think rationally
Systems that act like humans Systems that act rationally
Rich & Knight, 1991
“The study of how to make computers do things at which, at the
moment, people are better”
14. What is the definition of AI?
Systems that think like
humans
Systems that think rationally
Systems that act like humans Systems that act rationally
Schalkoff, 1990
“A field of study that seeks to explain and emulate intelligent behavior
in terms of computational processes”
15. What is the definition of AI?
Systems that think like
humans
Systems that think rationally
Systems that act like humans Systems that act rationally
Winston, 1992
“The study of the computations that make it possible to perceive,
reason, and act”
16. Approach 1: Acting Humanly
• Turing test: ultimate test for acting humanly
– Computer and human both interrogated by judge
– Computer passes test if judge can’t tell the difference
17. How effective is this test?
• Agent must:
– Have command of language
– Have wide range of knowledge
– Demonstrate human traits (humor, emotion)
– Be able to reason
– Be able to learn
• Loebner prize competition is modern version of
Turing Test
– Example: Alice, Loebner prize winner for 2000 and
2001
18. Chinese Room Argument
Imagine you are sitting in a room with a library of rule books, a bunch of blank exercise
books, and a lot of writing utensils. Your only contact with the external world is
through two slots in the wall labeled ``input'' and ``output''. Occasionally, pieces of
paper with Chinese characters come into your room through the ``input'' slot. Each
time a piece of paper comes in through the input slot your task is to find the section in
the rule books that matches the pattern of Chinese characters on the piece of paper.
The rule book will tell you which pattern of characters to inscribe the appropriate
pattern on a blank piece of paper. Once you have inscribed the appropriate pattern
according to the rule book your task is simply to push it out the output slot.
By the way, you don't understand Chinese, nor are you aware that the symbols that
you are manipulating are Chinese symbols.
In fact, the Chinese characters which you have been receiving as input have been
questions about a story and the output you have been producing has been the
appropriate, perhaps even "insightful," responses to the questions asked. Indeed, to
the outside questioners your output has been so good that they are convinced that
whoever (or whatever) has been producing the responses to their queries must be a
native speaker of, or at least extremely fluent in, Chinese.
19. Do you understand Chinese?
• Searle says NO
• What do you think?
• Is this a refutation of the possibility of AI?
• The Systems Reply
– The individual is just part of the overall system,
which does understand Chinese
• The Robot Reply
– Put same capabilities in a robot along with
perceiving, talking, etc. This agent would seem to
have genuine understanding and mental states.
20. Approach 2: Thinking Humanly
• Requires knowledge of brain function
• What level of abstraction?
• How can we validate this
• This is the focus of Cognitive Science
21. Approach 3: Thinking Rationally
• Aristotle attempted this
• What are correct arguments or thought
processes?
• Provided foundation of much of AI
• Not all intelligent behavior controlled by logic
• What is our goal? What is the purpose of
thinking?
22. Approach 4: Acting Rationally
• Act to achieve goals, given set of beliefs
• Rational behavior is doing the “right thing”
– Thing which expects to maximize goal
achievement
• This is approach adopted by Russell & Norvig
23. Foundations of AI
• Philosophy
– 450 BC, Socrates asked for algorithm to distinguish pious from non-
pious individuals
– Aristotle developed laws for reasoning
• Mathematics
– 1847, Boole introduced formal language for making logical inference
• Economics
– 1776, Smith views economies as consisting of agents maximizing their
own well being (payoff)
• Neuroscience
– 1861, Study how brains process information
• Psychology
– 1879, Cognitive psychology initiated
• Linguistics
– 1957, Skinner studied behaviorist approach to language learning
24. History of AI
• CS-based AI started with “Dartmouth Conference” in 1956
• Attendees
– John McCarthy
• LISP, application of logic to reasoning
– Marvin Minsky
• Popularized neural networks
• Slots and frames
• The Society of the Mind
– Claude Shannon
• Computer checkers
• Information theory
• Open-loop 5-ball juggling
– Allen Newell and Herb Simon
• General Problem Solver
25. AI Questions
• Can we make something that is as intelligent as a human?
• Can we make something that is as intelligent as a bee?
• Can we make something that is evolutionary, self improving,
autonomous, and flexible?
• Can we save this plant $20M/year by pattern recognition?
• Can we save this bank $50M/year by automatic fraud
detection?
• Can we start a new industry of handwriting recognition
agents?
26. Which of these exhibits intelligence?
• You beat somebody at chess.
• You prove a mathematical theorem using a set of known axioms.
• You need to buy some supplies, meet three different colleagues,
return books to the library, and exercise. You plan your day in such a
way that everything is achieved in an efficient manner.
• You are a lawyer who is asked to defend someone. You recall three
similar cases in which the defendant was guilty, and you turn down
the potential client.
• A stranger passing you on the street notices your watch and asks,
“Can you tell me the time?” You say, “It is 3:00.”
• You are told to find a large Phillips screwdriver in a cluttered
workroom. You enter the room (you have never been there before),
search without falling over objects, and eventually find the
screwdriver.
27. Which of these exhibits intelligence?
• You are a six-month-old infant. You can produce sounds with your
vocal organs, and you can hear speech sounds around you, but you
do not know how to make the sounds you are hearing. In the next
year, you figure out what the sounds of your parents' language are
and how to make them.
• You are a one-year-old child learning Arabic. You hear strings of
sounds and figure out that they are associated with particular
meanings in the world. Within two years, you learn how to segment
the strings into meaningful parts and produce your own words and
sentences.
• Someone taps a rhythm, and you are able to beat along with it and
to continue it even after it stops.
• You are some sort of primitive invertebrate. You know nothing
about how to move about in your world, only that you need to find
food and keep from bumping into walls. After lots of reinforcement
and punishment, you get around just fine.
28. Which of these can currently be done?
• Play a decent game of table tennis
• Drive autonomously along a curving mountain road
• Drive autonomously in the center of Cairo
• Play a decent game of bridge
• Discover and prove a new mathematical theorem
• Write an intentionally funny story
• Give competent legal advice in a specialized area of law
• Translate spoken English into spoken Swedish in real time
• Plan schedule of operations for a NASA spacecraft
• Defeat the world champion in chess
29. Components of an AI System
An agent perceives its environment
through sensors and acts on the
environment through actuators.
Human: sensors are eyes, ears,
actuators (effectors) are hands,
legs, mouth.
Robot: sensors are cameras, sonar,
lasers, ladar, bump, effectors are
grippers, manipulators, motors
The agent’s behavior is described by its
function that maps percept to action.
30. Rationality
• A rational agent does the right thing
(what is this?)
• A fixed performance measure evaluates the
sequence of observed action effects on the
environment
31. PEAS
• Use PEAS to describe task
– Performance measure
– Environment
– Actuators
– Sensors
33. Environment Properties
• Fully observable vs. partially observable
• Deterministic vs. stochastic / strategic
• Episodic vs. sequential
• Static vs. dynamic
• Discrete vs. continuous
• Single agent vs. multiagent
34. Environment Examples
Environment Obser
vable
Determi
nistic
Episodic Static Discrete Agents
Chess with a clock
Chess without a clock
Fully observable vs. partially observable
Deterministic vs. stochastic / strategic
Episodic vs. sequential
Static vs. dynamic
Discrete vs. continuous
Single agent vs. multiagent
35. Environment Examples
Environment Obser
vable
Determi
nistic
Episodic Static Discrete Agents
Chess with a clock Fully Strategic Sequential Semi Discrete Multi
Chess without a clock Fully Strategic Sequential Static Discrete Multi
Fully observable vs. partially observable
Deterministic vs. stochastic / strategic
Episodic vs. sequential
Static vs. dynamic
Discrete vs. continuous
Single agent vs. multiagent
36. Environment Examples
Fully observable vs. partially observable
Deterministic vs. stochastic / strategic
Episodic vs. sequential
Static vs. dynamic
Discrete vs. continuous
Single agent vs. multiagent
Environment Obser
vable
Determi
nistic
Episodic Static Discrete Agents
Chess with a clock Fully Strategic Sequential Semi Discrete Multi
Chess without a clock Fully Strategic Sequential Static Discrete Multi
Poker
37. Environment Examples
Fully observable vs. partially observable
Deterministic vs. stochastic / strategic
Episodic vs. sequential
Static vs. dynamic
Discrete vs. continuous
Single agent vs. multiagent
Environment Obser
vable
Determi
nistic
Episodic Static Discrete Agents
Chess with a clock Fully Strategic Sequential Semi Discrete Multi
Chess without a clock Fully Strategic Sequential Static Discrete Multi
Poker Partial Strategic Sequential Static Discrete Multi
38. Environment Examples
Fully observable vs. partially observable
Deterministic vs. stochastic / strategic
Episodic vs. sequential
Static vs. dynamic
Discrete vs. continuous
Single agent vs. multiagent
Environment Obser
vable
Determi
nistic
Episodic Static Discrete Agents
Chess with a clock Fully Strategic Sequential Semi Discrete Multi
Chess without a clock Fully Strategic Sequential Static Discrete Multi
Poker Partial Strategic Sequential Static Discrete Multi
Backgammon
39. Environment Examples
Fully observable vs. partially observable
Deterministic vs. stochastic / strategic
Episodic vs. sequential
Static vs. dynamic
Discrete vs. continuous
Single agent vs. multiagent
Environment Obser
vable
Determi
nistic
Episodic Static Discrete Agents
Chess with a clock Fully Strategic Sequential Semi Discrete Multi
Chess without a clock Fully Strategic Sequential Static Discrete Multi
Poker Partial Strategic Sequential Static Discrete Multi
Backgammon Fully Stochast
ic
Sequential Static Discrete Multi
40. Environment Examples
Fully observable vs. partially observable
Deterministic vs. stochastic / strategic
Episodic vs. sequential
Static vs. dynamic
Discrete vs. continuous
Single agent vs. multiagent
Environment Obser
vable
Determi
nistic
Episodic Static Discrete Agents
Chess with a clock Fully Strategic Sequential Semi Discrete Multi
Chess without a clock Fully Strategic Sequential Static Discrete Multi
Poker Partial Strategic Sequential Static Discrete Multi
Backgammon Fully Stochast
ic
Sequential Static Discrete Multi
Taxi driving Partial Stochast
ic
Sequential Dyna
mic
Continu
ous
Multi
41. Environment Examples
Fully observable vs. partially observable
Deterministic vs. stochastic / strategic
Episodic vs. sequential
Static vs. dynamic
Discrete vs. continuous
Single agent vs. multiagent
Environment Obser
vable
Determi
nistic
Episodic Static Discrete Agents
Chess with a clock Fully Strategic Sequential Semi Discrete Multi
Chess without a clock Fully Strategic Sequential Static Discrete Multi
Poker Partial Strategic Sequential Static Discrete Multi
Backgammon Fully Stochast
ic
Sequential Static Discrete Multi
Taxi driving Partial Stochast
ic
Sequential Dyna
mic
Continu
ous
Multi
Medical diagnosis
42. Environment Examples
Fully observable vs. partially observable
Deterministic vs. stochastic / strategic
Episodic vs. sequential
Static vs. dynamic
Discrete vs. continuous
Single agent vs. multiagent
Environment Obser
vable
Determi
nistic
Episodic Static Discrete Agents
Chess with a clock Fully Strategic Sequential Semi Discrete Multi
Chess without a clock Fully Strategic Sequential Static Discrete Multi
Poker Partial Strategic Sequential Static Discrete Multi
Backgammon Fully Stochast
ic
Sequential Static Discrete Multi
Taxi driving Partial Stochast
ic
Sequential Dyna
mic
Continu
ous
Multi
Medical diagnosis Partial Stochast
ic
Episodic Static Continu
ous
Single
43. Environment Examples
Fully observable vs. partially observable
Deterministic vs. stochastic / strategic
Episodic vs. sequential
Static vs. dynamic
Discrete vs. continuous
Single agent vs. multiagent
Environment Obser
vable
Determi
nistic
Episodic Static Discrete Agents
Chess with a clock Fully Strategic Sequential Semi Discrete Multi
Chess without a clock Fully Strategic Sequential Static Discrete Multi
Poker Partial Strategic Sequential Static Discrete Multi
Backgammon Fully Stochast
ic
Sequential Static Discrete Multi
Taxi driving Partial Stochast
ic
Sequential Dyna
mic
Continu
ous
Multi
Medical diagnosis Partial Stochast
ic
Episodic Static Continu
ous
Single
Image analysis
44. Environment Examples
Environment Obser
vable
Determi
nistic
Episodic Static Discrete Agents
Chess with a clock Fully Strategic Sequential Semi Discrete Multi
Chess without a clock Fully Strategic Sequential Static Discrete Multi
Poker Partial Strategic Sequential Static Discrete Multi
Backgammon Fully Stochast
ic
Sequential Static Discrete Multi
Taxi driving Partial Stochast
ic
Sequential Dyna
mic
Continu
ous
Multi
Medical diagnosis Partial Stochast
ic
Episodic Static Continu
ous
Single
Image analysis Fully Determi
nistic
Episodic Semi Discrete Single
Fully observable vs.
partially observable
Deterministic vs.
stochastic / strategic
Episodic vs. sequential
Static vs. dynamic
Discrete vs. continuous
Single agent vs.
multiagent
45. Environment Examples
Fully observable vs.
partially observable
Deterministic vs.
stochastic / strategic
Episodic vs. sequential
Static vs. dynamic
Discrete vs. continuous
Single agent vs.
multiagent
Environment Obser
vable
Determi
nistic
Episodic Static Discrete Agents
Chess with a clock Fully Strategic Sequential Semi Discrete Multi
Chess without a clock Fully Strategic Sequential Static Discrete Multi
Poker Partial Strategic Sequential Static Discrete Multi
Backgammon Fully Stochast
ic
Sequential Static Discrete Multi
Taxi driving Partial Stochast
ic
Sequential Dyna
mic
Continu
ous
Multi
Medical diagnosis Partial Stochast
ic
Episodic Static Continu
ous
Single
Image analysis Fully Determi
nistic
Episodic Semi Discrete Single
Robot part picking
46. Environment Examples
Fully observable vs.
partially observable
Deterministic vs.
stochastic / strategic
Episodic vs. sequential
Static vs. dynamic
Discrete vs. continuous
Single agent vs.
multiagent
Environment Obser
vable
Determi
nistic
Episodic Static Discrete Agents
Chess with a clock Fully Strategic Sequential Semi Discrete Multi
Chess without a clock Fully Strategic Sequential Static Discrete Multi
Poker Partial Strategic Sequential Static Discrete Multi
Backgammon Fully Stochast
ic
Sequential Static Discrete Multi
Taxi driving Partial Stochast
ic
Sequential Dyna
mic
Continu
ous
Multi
Medical diagnosis Partial Stochast
ic
Episodic Static Continu
ous
Single
Image analysis Fully Determi
nistic
Episodic Semi Discrete Single
Robot part picking Fully Determi
nistic
Episodic Semi Discrete Single
47. Environment Examples
Fully observable vs.
partially observable
Deterministic vs.
stochastic / strategic
Episodic vs. sequential
Static vs. dynamic
Discrete vs. continuous
Single agent vs.
multiagent
Environment Obser
vable
Determi
nistic
Episodic Static Discrete Agents
Chess with a clock Fully Strategic Sequential Semi Discrete Multi
Chess without a clock Fully Strategic Sequential Static Discrete Multi
Poker Partial Strategic Sequential Static Discrete Multi
Backgammon Fully Stochast
ic
Sequential Static Discrete Multi
Taxi driving Partial Stochast
ic
Sequential Dyna
mic
Continu
ous
Multi
Medical diagnosis Partial Stochast
ic
Episodic Static Continu
ous
Single
Image analysis Fully Determi
nistic
Episodic Semi Discrete Single
Robot part picking Fully Determi
nistic
Episodic Semi Discrete Single
Interactive English
tutor
48. Environment Examples
Fully observable vs.
partially observable
Deterministic vs.
stochastic / strategic
Episodic vs. sequential
Static vs. dynamic
Discrete vs. continuous
Single agent vs.
multiagent
Environment Obser
vable
Determi
nistic
Episodic Static Discrete Agents
Chess with a clock Fully Strategic Sequential Semi Discrete Multi
Chess without a clock Fully Strategic Sequential Static Discrete Multi
Poker Partial Strategic Sequential Static Discrete Multi
Backgammon Fully Stochast
ic
Sequential Static Discrete Multi
Taxi driving Partial Stochast
ic
Sequential Dyna
mic
Continu
ous
Multi
Medical diagnosis Partial Stochast
ic
Episodic Static Continu
ous
Single
Image analysis Fully Determi
nistic
Episodic Semi Discrete Single
Robot part picking Fully Determi
nistic
Episodic Semi Discrete Single
Interactive English
tutor
Partial Stochast
ic
Sequential Dyna
mic
Discrete Multi
49. Agent Types
• Types of agents (increasing in generality and
ability to handle complex environments)
– Simple reflex agents
– Reflex agents with state
– Goal-based agents
– Utility-based agents
– Learning agent
50. Simple Reflex Agent
• Use simple “if
then” rules
• Can be short
sighted
SimpleReflexAgent(percept)
state = InterpretInput(percept)
rule = RuleMatch(state, rules)
action = RuleAction(rule)
Return action
51. Example: Vacuum Agent
• Performance?
– 1 point for each square cleaned in time T?
– #clean squares per time step - #moves per time step?
• Environment: vacuum, dirt, multiple areas defined by square regions
• Actions: left, right, suck, idle
• Sensors: location and contents
– [A, dirty]
• Rational is not omniscient
– Environment may be partially observable
• Rational is not clairvoyant
– Environment may be stochastic
• Thus Rational is not always successful
52. Reflex Vacuum Agent
• If status=Dirty then return Suck
else if location=A then return Right
else if location=B then right Left
53. Reflex Agent With State
• Store previously-
observed information
• Can reason about
unobserved aspects of
current state
ReflexAgentWithState(percept)
state = UpdateDate(state,action,percept)
rule = RuleMatch(state, rules)
action = RuleAction(rule)
Return action
54. Reflex Vacuum Agent
• If status=Dirty then Suck
else if have not visited other square in >3
time units, go there
55. Goal-Based Agents
• Goal reflects
desires of agents
• May project actions
to see if consistent
with goals
• Takes time, world
may change during
reasoning
58. Xavier mail delivery robot
• Performance: Completed tasks
• Environment: See for yourself
• Actuators: Wheeled robot actuation
• Sensors: Vision, sonar, dead reckoning
• Reasoning: Markov model induction, A*
search, Bayes classification
59. Pathfinder Medical Diagnosis System
• Performance: Correct Hematopathology
diagnosis
• Environment: Automate human diagnosis,
partially observable, deterministic, episodic,
static, continuous, single agent
• Actuators: Output diagnoses and further test
suggestions
• Sensors: Input symptoms and test results
• Reasoning: Bayesian networks, Monte-Carlo
simulations
60. TDGammon
• Performance: Ratio of wins to losses
• Environment: Graphical output showing dice roll
and piece movement, fully observable, stochastic,
sequential, static, discrete, multiagent
World Champion Backgammon Player
• Sensors: Keyboard input
• Actuator: Numbers representing moves of pieces
• Reasoning: Reinforcement learning, neural
networks
61. Alvinn
• Performance: Stay in lane, on road, maintain
speed
• Environment: Driving Hummer on and off road
without manual control (Partially observable,
stochastic, episodic, dynamic, continuous,
single agent), Autonomous automobile
• Actuators: Speed, Steer
• Sensors: Stereo camera input
• Reasoning: Neural networks
62. Talespin
• Performance: Entertainment value of generated story
• Environment: Generate text-based stories that are creative and
understandable
– One day Joe Bear was hungry. He asked his friend Irving Bird where some
honey was. Irving told him there was a beehive in the oak tree. Joe
threatened to hit Irving if he didn't tell him where some honey was.
– Henry Squirrel was thirsty. He walked over to the river bank where his good
friend Bill Bird was sitting. Henry slipped and fell in the river. Gravity
drowned. Joe Bear was hungry. He asked Irving Bird where some honey
was. Irving refused to tell him, so Joe offered to bring him a worm if he'd
tell him where some honey was. Irving agreed. But Joe didn't know where
any worms were, so he asked Irving, who refused to say. So Joe offered to
bring him a worm if he'd tell him where a worm was. Irving agreed. But Joe
didn't know where any worms were, so he asked Irving, who refused to
say. So Joe offered to bring him a worm if he'd tell him where a worm
was…
• Actuators: Add word/phrase, order parts of story
• Sensors: Dictionary, Facts and relationships
stored in database
• Reasoning: Planning
63. Webcrawler Softbot
• Search web for items of interest
• Perception: Web pages
• Reasoning: Pattern matching
• Action: Select and traverse hyperlinks
64. Other Example AI Systems
• Translation of Caterpillar
truck manuals into 20
languages
• Shuttle packing
• Military planning (Desert
Storm)
• Intelligent vehicle
highway negotiation
• Credit card transaction
monitoring
• Billiards robot
• Juggling robot
• Credit card fraud
detection
• Lymphatic system
diagnoses
• Mars rover
• Sky survey galaxy data
analysis
65. Other Example AI Systems
• Knowledge
Representation
• Search
• Problem solving
• Planning
• Machine learning
• Natural language
processing
• Uncertainty reasoning
• Computer Vision
• Robotics
Editor's Notes
What level of abstraction? Knowledge, circuitry, chemical?
LADAR is Laser Detection and Ranging
Light radar by uses light
Fully observable vs. partially observable
Environment sensors provide access to complete state of the
relevant environment at each point in time
Deterministic vs. stochastic / strategic
If next state completely determined by current and action,
then deterministic, otherwise stochastic
If deterministic except for actions of other agents,
then strategic
Episodic vs. sequential
Episodic, action choice depends only on current state
Sequential, current action may affect future actions
Static vs. dynamic
Dynamic, environment can change while agent is deliberating
Discrete vs. continuous
Applied to state, time, percepts, or actions
The way the information is represented
Single agent vs. multiagent
How distinguish agent from environment?
if other's behavior maximizes its performance based on
agent, then it is multiagent