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  1. INTELLIGENT Systems Prof. Magdy M. Aboul-Ela Information Systems Department Faculty of Management and Information Systems French University in Egypt Email:

  2. Textbook S. Russell and P. Norvig Artificial Intelligence: A Modern Approach Prentice Hall, 2003, Second Edition

  3. Outline • Course overview • What is AI? • A brief history • The state of the art

  4. Course overview • Introduction and Agents • Search • Knowledge Representation • Symbolic Logic • Prolog • Applications

  5. What is an Artificial Intelligence (AI) ? • AI attempts to build intelligent entities • AI is both science and engineering: • the science of understanding intelligent entities — of developing theories which attempt to explain and predict the nature of such entities; • the engineering of intelligent entities.

  6. What is AI? Views of AI fall into four categories: Thinking humanly Thinking rationally Acting humanly Acting rationally

  7. Acting humanly: Turing Test • Turing (1950) "Computing machinery and intelligence": • "Can machines think?"  "Can machines behave intelligently?" • Operational test for intelligent behavior: the Imitation Game • Predicted that by 2000, a machine might have a 30% chance of fooling a lay person for 5 minutes • Anticipated all major arguments against AI in following 50 years • Suggested major components of AI: knowledge, reasoning, language understanding, learning

  8. Thinking humanly: cognitive modeling • 1960s "cognitive revolution": information-processing psychology • Requires scientific theories of internal activities of the brain • -- How to validate? Requires 1) Predicting and testing behavior of human subjects (top-down) or 2) Direct identification from neurological data (bottom-up) • Both approaches (roughly, Cognitive Science and Cognitive Neuroscience) are now distinct from AI

  9. Thinking rationally: "laws of thought" • Aristotle: what are correct arguments/thought processes? • Several Greek schools developed various forms of logic: notation and rules of derivation for thoughts; may or may not have proceeded to the idea of mechanization • Direct line through mathematics and philosophy to modern AI • Problems: • Not all intelligent behavior is mediated by logical deliberation • What is the purpose of thinking? What thoughts should I have?

  10. Acting rationally: rational agent • Rational behavior: doing the right thing • The right thing: that which is expected to maximize goal achievement, given the available information • Doesn't necessarily involve thinking – e.g., blinking reflex – but thinking should be in the service of rational action

  11. Rational agents • An agent is an entity that perceives and acts This course is about designing rational agents • Abstractly, an agent is a function from percept histories to actions: [f: P*A] For any given class of environments and tasks, we seek the agent (or class of agents) with the best performance Caveat: computational limitations make perfect rationality unachievable  design best program for given machine resources

  12. AI prehistory • Philosophy Logic, methods of reasoning, mind as physical system foundations of learning, language, rationality • Mathematics Formal representation and proof algorithms, computation, (un)decidability, (in)tractability, probability • Economics utility, decision theory • Neuroscience physical substrate for mental activity • Psychology phenomena of perception and motor control, experimental techniques • Computer building fast computers engineering • Control theory design systems that maximize an objective function over time • Linguistics knowledge representation, grammar

  13. Abridged history of AI • 1943 McCulloch & Pitts: Boolean circuit model of brain • 1950 Turing's "Computing Machinery and Intelligence" • 1956 Dartmouth meeting: "Artificial Intelligence" adopted • 1952—69 Look, Ma, no hands! • 1950s Early AI programs, including Samuel's checkers program, Newell & Simon's Logic Theorist, Gelernter's Geometry Engine • 1965 Robinson's complete algorithm for logical reasoning • 1966—73 AI discovers computational complexity Neural network research almost disappears • 1969—79 Early development of knowledge-based systems • 1980-- AI becomes an industry • 1986-- Neural networks return to popularity • 1987-- AI becomes a science • 1995-- The emergence of intelligent agents

  14. State of the art • Deep Blue defeated the reigning world chess champion Garry Kasparov in 1997 • Proved a mathematical conjecture (Robbins conjecture) unsolved for decades • No hands across America (driving autonomously 98% of the time from Pittsburgh to San Diego) • During the 1991 Gulf War, US forces deployed an AI logistics planning and scheduling program that involved up to 50,000 vehicles, cargo, and people • NASA's on-board autonomous planning program controlled the scheduling of operations for a spacecraft • Proverb solves crossword puzzles better than most humans

  15. Intelligent Agents

  16. Outline • Agents and environments • Rationality • PEAS (Performance measure, Environment, Actuators, Sensors) • Environment types • Agent types

  17. Agents • An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators • Human agent: eyes, ears, and other organs for sensors; hands, • legs, mouth, and other body parts for actuators • Robotic agent: cameras and infrared range finders for sensors; • various motors for actuators

  18. Agents and environments • The agentfunction maps from percept histories to actions: [f: P* A] • The agentprogram runs on the physical architecture to produce f • agent = architecture + program

  19. Vacuum-cleaner world • Percepts: location and contents, e.g., [A,Dirty] • Actions: Left, Right, Suck, NoOp

  20. Rational agents • An agent should strive to "do the right thing", based on what it can perceive and the actions it can perform. The right action is the one that will cause the agent to be most successful • Performance measure: An objective criterion for success of an agent's behavior • E.g., performance measure of a vacuum-cleaner agent could be amount of dirt cleaned up, amount of time taken, amount of electricity consumed, amount of noise generated, etc.

  21. Rational agents • RationalAgent: For each possible percept sequence, a rational agent should select an action that is expected to maximize its performance measure, given the evidence provided by the percept sequence and whatever built-in knowledge the agent has.

  22. Rational agents • Rationality is distinct from omniscience (all-knowing with infinite knowledge) • Agents can perform actions in order to modify future percepts so as to obtain useful information (information gathering, exploration) • An agent is autonomous if its behavior is determined by its own experience (with ability to learn and adapt)

  23. PEAS • PEAS: Performance measure, Environment, Actuators, Sensors • Must first specify the setting for intelligent agent design • Consider, e.g., the task of designing an automated taxi driver: Performance measure • Environment • Actuators • Sensors

  24. PEAS • Must first specify the setting for intelligent agent design • Consider, e.g., the task of designing an automated taxi driver: Performance measure: Safe, fast, legal, comfortable trip, maximize profits • Environment: Roads, other traffic, pedestrians, customers Actuators: Steering wheel, accelerator, brake, signal, horn Sensors: Cameras, sonar, speedometer, GPS, odometer, engine sensors, keyboard

  25. PEAS • Agent: Medical diagnosis system • Performance measure: Healthy patient, minimize costs, lawsuits • Environment: Patient, hospital, staff • Actuators: Screen display (questions, tests, diagnoses, treatments, referrals) • Sensors: Keyboard (entry of symptoms, findings, patient's answers)

  26. PEAS • Agent: Part-picking robot • Performance measure: Percentage of parts in correct bins • Environment: Conveyor belt with parts, bins • Actuators: Jointed arm and hand • Sensors: Camera, joint angle sensors

  27. PEAS • Agent: Interactive English tutor • Performance measure: Maximize student's score on test • Environment: Set of students • Actuators: Screen display (exercises, suggestions, corrections) • Sensors: Keyboard

  28. Environment types • Fully observable (vs. partially observable): An agent's sensors give it access to the complete state of the environment at each point in time. • Deterministic (vs. stochastic): The next state of the environment is completely determined by the current state and the action executed by the agent. (If the environment is deterministic except for the actions of other agents, then the environment is strategic) • Episodic (vs. sequential): The agent's experience is divided into atomic "episodes" (each episode consists of the agent perceiving and then performing a single action), and the choice of action in each episode depends only on the episode itself.

  29. Environment types • Static (vs. dynamic): The environment is unchanged while an agent is deliberating. (The environment is semidynamic if the environment itself does not change with the passage of time but the agent's performance score does) • Discrete (vs. continuous): A limited number of distinct, clearly defined percepts and actions. • Single agent (vs. multiagent): An agent operating by itself in an environment.

  30. Environment types Chess with Chess without Taxi driving a clock a clock Fully observable Yes Yes No Deterministic Strategic Strategic No Episodic No No No Static Semi Yes No Discrete Yes Yes No Single agent No No No • The environment type largely determines the agent design • The real world is (of course) partially observable, stochastic, sequential, dynamic, continuous, multi-agent

  31. Agent functions and programs • An agent is completely specified by the agent function mapping percept sequences to actions • One agent function (or a small equivalence class) is rational • Aim: find a way to implement the rational agent function concisely

  32. Table-lookup agent • Drawbacks: • Huge table • Take a long time to build the table • No autonomy • Even with learning, need a long time to learn the table entries

  33. Agent types • Four basic types in order of increasing generality: • Simple reflex agents • Model-based reflex agents • Goal-based agents • Utility-based agents • Learning agents

  34. Simple reflex agents • Simple reflex agents act only on the basis of the current percept. The agent function is based on the condition-action rule: if condition then action. • This agent function only succeeds when the environment is fully observable. Some reflex agents can also contain information on their current state which allows them to disregard conditions whose actuators are already triggered.

  35. Simple reflex agents

  36. Model-based agents • Model-based agents can handle partially observable environments. Its current state is stored inside the agent maintaining some kind of structure which describes the part of the world which cannot be seen. • This behavior requires information on how the world behaves and works. This additional information completes the “World View” model. • A model-based reflex agent keeps track of the current state of the world using an internal model. It then chooses an action in the same way as the reflex agent.

  37. Model-based reflex agents

  38. Goal-based agents • Goal-based agents are model-based agents which store information regarding situations that are desirable. • This allows the agent a way to choose among multiple possibilities, selecting the one which reaches a goal state.

  39. Goal-based agents

  40. Utility-based agents • Goal-based agents only distinguish between goal states and non-goal states. It is possible to define a measure of how desirable a particular state is. • This measure can be obtained through the use of a utility function which maps a state to a measure of the utility of the state.

  41. Utility-based agents

  42. Learning agents • Learning has an advantage that it allows the agents to initially operate in unknown environments and to become more competent than its initial knowledge alone might allow.

  43. Learning agents

  44. Other classes of intelligent agents There are some of the sub-agents that may be a part of an Intelligent Agent or a complete Intelligent Agent in themselves are: • Decision Agents (that are geared to decision making); • Input Agents (that process and make sense of sensor inputs - e.g. neural network based agents); • Processing Agents (that solve a problem like speech recognition); • Spatial Agents (that relate to the physical real-world); • World Agents (that incorporate a combination of all the other classes of agents to allow autonomous behaviors). • Believable agents - An agent exhibiting a personality via the use of an artificial character (the agent is embedded) for the interaction. • Physical Agents - A physical agent is an entity which percepts through sensors and acts through actuators. • Temporal Agents - A temporal agent may use time based stored information to offer instructions or data acts to a computer program or human being and takes program inputs percepts to adjust its next behaviors.

  45. Multi-Agent System (MAS) • A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. • Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. • Examples of problems which are appropriate to multi-agent systems research include online trading, disaster response, and modelling social structures.

  46. Multi-Agent System (MAS) The agents in a multi-agent system have several important characteristics: • Autonomy: the agents are at least partially autonomous • Local views: no agent has a full global view of the system, or the system is too complex for an agent to make practical use of such knowledge • Decentralization: there is no designated controlling agent (or the system is effectively reduced to a monolithic system) • Typically multi-agent systems research refers to software agents. However, the agents in a multi-agent system could equally well be robots, humans or human teams. A multi-agent system may contain combined human-agent teams. • Multi-agent systems can manifest self-organization and complex behaviors even when the individual strategies of all their agents are simple. • Agents can share knowledge using any agreed language, within the constraints of the system's communication protocol. Example languages are Knowledge Query Manipulation Language (KQML) or FIPA's Agent Communication Language (ACL).

  47. Solving problems by searching

  48. Outline • Problem-solving agents • Problem types • Problem formulation • Example problems • Basic search algorithms

  49. Problem-solving agents

  50. Example: Romania • On holiday in Romania; currently in Arad. • Flight leaves tomorrow from Bucharest • Formulate goal: • be in Bucharest • Formulate problem: • states: various cities • actions: drive between cities • Find solution: • sequence of cities, e.g., Arad, Sibiu, Fagaras, Bucharest