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Artificial Intelligence Chapter 2 Agents AI chapter 2 What is an (Intelligent) Agent? • An over-used, over-loaded, and misused term. Anything that can be viewed as perceiving its environment through sensors and acting upon that environment through its effectors to maximize progress towards its goals. • Perception • Sensors receive input from environment • Keyboard clicks • Camera data • Action • Actuators impact the environment • Move a robotic arm • Generate output for computer display AI chapter 2 Perception • Percept • Perceptual inputs at an instant • May include perception of internal state • Percept Sequence • Complete history of all prior percepts • Do you need a percept sequence to play Chess? AI chapter 2 What is an (Intelligent) Agent? • PAGE (Percepts, Actions, Goals, Environment) • Task-specific & specialized: well-defined goals and environment • The notion of an agent is meant to be a tool for analyzing systems, • It is not a different hardware or new programming languages AI chapter 2 Intelligent Agents and Artificial Intelligence • Distributed decision-making and control AI chapter 2 effectors sensors • Example: Human mind as network of thousands or millions of agents working in parallel. To produce real artificial intelligence, this school holds, we should build computer systems that also Agency contain many agents and systems for arbitrating among the agents' competing results. Agent Types We can split agent research into two main strands: • Distributed Artificial Intelligence (DAI) – Multi-Agent Systems (MAS) (1980 – 1990) • Much broader notion of "agent" (1990’s – present) AI chapter 2 Intelligent Agents Chapter 2 Outline • Agents and environments • Rationality • PEAS (Performance measure, Environment, Actuators, Sensors) • Environment types • Agent types AI chapter 2 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 • AI chapter 2 Agents and environments • The agent function maps from percept histories to actions: • [f: P* A] • The agent program runs on the physical architecture to produce f • • agent = architecture + program AI chapter 2 • Vacuum-cleaner world • Percepts: location and contents, e.g., [A,Dirty] • • Actions: Left, Right, Suck, NoOp • AI chapter 2 A vacuum-cleaner agent • \input{tables/vacuum-agent-function-table} • AI chapter 2 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. • AI chapter 2 Rational agents • Rational Agent: 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. • AI chapter 2 Rational agents • Rationality is distinct from omniscience (allknowing 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) • AI chapter 2 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 AI chapter 2 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 • AI chapter 2 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) AI chapter 2 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 AI chapter 2 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 AI chapter 2 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. • AI chapter 2 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. • AI chapter 2 Environment types Fully observable Yes Deterministic Episodic Static Discrete Yes Single agent • • • • Chess with Chess without a clock a clock Yes No Strategic Strategic No No No Semi Yes Yes No No No Taxi driving 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 AI chapter 2 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 • AI chapter 2 Table-lookup agent • \input{algorithms/table-agent-algorithm} • • 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 AI chapter 2 Agent program for a vacuum-cleaner agent • \input{algorithms/reflex-vacuum-agentalgorithm} • AI chapter 2 Agent types • Four basic types in order of increasing generality: • • Simple reflex agents • Model-based reflex agents • Goal-based agents • Utility-based agents AI chapter 2 Simple reflex agents AI chapter 2 Simple reflex agents • \input{algorithms/d-agent-algorithm} • AI chapter 2 Model-based reflex agents AI chapter 2 Model-based reflex agents • \input{algorithms/d+-agent-algorithm} • AI chapter 2 Goal-based agents AI chapter 2 Utility-based agents AI chapter 2 Learning agents AI chapter 2 Rational Agents How to design this? Sensors percepts ? Agent Environment actions Effectors AI chapter 2 Interacting Agents Collision Avoidance Agent (CAA) • Goals: Avoid running into obstacles • Percepts ? • Sensors? • Effectors ? •Lane Actions Keeping ? Agent (LKA) •• Goals: Stay inFreeway current lane Environment: • • • • • Percepts ? Sensors? Effectors ? Actions ? Environment: Freeway AI chapter 2 Interacting Agents Collision Avoidance Agent (CAA) • Goals: Avoid running into obstacles • Percepts: Obstacle distance, velocity, trajectory • Sensors: Vision, proximity sensing • Effectors: Steering Wheel, Accelerator, Brakes, Horn,(LKA) Headlights Lane Keeping Agent •• Goals: in current laneup, brake, blow horn, Actions: Stay Steer, speed • Percepts: Lane center, lane boundaries signal (headlights) • Sensors: Vision •• Environment: Freeway Effectors: Steering Wheel, Accelerator, Brakes • Actions: Steer, speed up, brake • Environment: Freeway AI chapter 2 Conflict Resolution by Action Selection Agents • Override: CAA overrides LKA • Arbitrate: if Obstacle is Close then CAA else LKA • Compromise: Choose action that satisfies both agents • Any combination of the above • Challenges: Doing the right thing AI chapter 2 The Right Thing = The Rational Action • Rational Action: The action that maximizes the expected value of the performance measure given the percept sequence to date • • • • • Rational Rational Rational Rational Rational = = = = = Best ? Optimal ? Omniscience ? Clairvoyant ? Successful ? AI chapter 2 The Right Thing = The Rational Action • Rational Action: The action that maximizes the expected value of the performance measure given the percept sequence to date • Rational = Best Yes, to the best of its knowledge • Rational = Optimal Yes, to the best of its abilities (incl. • Rational Omniscience(全知) its constraints) • Rational Clairvoyant(有超人力) • Rational Successful AI chapter 2 Behavior and performance of IAs • Perception (sequence) to Action Mapping: f : P* A • Ideal mapping: specifies which actions an agent ought to take at any point in time • Description: Look-Up-Table, Closed Form, etc. • Performance measure: a subjective measure to characterize how successful an agent is (e.g., speed, power usage, accuracy, money, etc.) AI chapter 2 Look up table Distanc Action e 10 5 2 No action Turn left 30 degrees Stop AI chapter 2 obstacle sensor agent Closed form • Output (degree of rotation) = F(distance) • E.g., F(d) = 10/d less than 1/10) (distance cannot be AI chapter 2 How is an Agent different from other software? • Agents are autonomous, that is, they act on behalf of the user • Agents contain some level of intelligence, from fixed rules to learning engines that allow them to adapt to changes in the environment chapter 2 • Agents don't only AIact reactively, but How is an Agent different from other software? • Agents have social ability, that is, they communicate with the user, the system, and other agents as required • Agents may also cooperate with other agents to carry out more complex tasks than they themselves can handle AI chapterfrom 2 • Agents may migrate one system to Environment Types • Characteristics • Accessible vs. inaccessible • Deterministic vs. nondeterministic • Episodic vs. nonepisodic • Hostile vs. friendly • Static vs. dynamic • Discrete vs. continuous AI chapter 2 Environment Types • Characteristics • Accessible vs. inaccessible • Sensors give access to complete state of the environment. • Deterministic vs. nondeterministic • The next state can be determined based on the current state and the action. AI chapter 2 • Episodic vs. nonepisodic (Sequential) Environment Types • Characteristics • Hostile vs. friendly • Static vs. dynamic • Dynamic if the environment changes during deliberation • Discrete vs. continuous • Chess vs. driving AI chapter 2 Environment types Environme Accessi Determin Episodi Static Discrete nt ble istic c Operating System Virtual Reality Office Environme nt AI chapter 2 Mars Environment types Environme Accessi Determin Episodi Static Discrete nt ble istic c Operating Yes Yes No No Yes System Virtual Reality Office Environme nt AI chapter 2 Mars Environment types Environme nt Operating System Virtual Reality Office Environme nt Mars Accessi Determin Episodi Static Discrete ble istic c Yes Yes No No Yes Yes Yes Yes/no No AI chapter 2 Yes/no Environment types Environme nt Operating System Virtual Reality Office Environme nt Mars Accessi Determin Episodi Static Discrete ble istic c Yes Yes No No Yes Yes Yes Yes/no No Yes/no No No No No AI chapter 2 No Environment types Environme Accessi Determin Episodi Static Discrete nt ble istic c Operating Yes Yes No No Yes System Virtual Yes Yes Yes/no No Yes/no Reality Office No No No No No Environme ntThe environment types largely determine the agent design. Mars No SemiAI chapter 2 No Semi No Structure of Intelligent Agents • Agent = architecture + program • Agent program: the implementation of f : P* A, the agent’s perception-action mapping function Skeleton-Agent(Percept) returns Action memory UpdateMemory(memory, Percept) Action AI chapter 2 Using a look-up-table to encode f : P* A • Example: Collision Avoidance • Sensors: • Effectors: 3 proximity sensors Steering Wheel, sensors Brakes • How to generate? • How large? • How to select action? AI chapter 2 obstacle agent Using a look-up-table to encode f : P* A • Example: Collision Avoidance • Sensors: • Effectors: sensors 3 proximity sensors Steering Wheel, Brakes obstacle agent • How to generate: for each p Pl Pm Pr generate an appropriate action, a S B • How large: size of table = #possible percepts times # possible actions = |Pl | AI chapter 2 Agent types • Reflex agents • Reflex agents with internal states • Goal-based agents • Utility-based agents AI chapter 2 Agent types • Reflex agents • Reactive: No memory • Reflex agents with internal states • W/o previous state, may not be able to make decision • E.g. brake lights at night. • Goal-based agents AI chapter 2 Agent types • Utility-based agents • How well can the goal be achieved (degree of happiness) • What to do if there are conflicting goals? • Speed and safety • Which goal should be selected if AI chapter 2 Reflex agents AI chapter 2 Reactive agents • Reactive agents do not have internal symbolic models. • Act by stimulus-response to the current state of the environment. • Each reactive agent is simple and interacts with others in a basic way. • Complex patterns of behavior emerge from their interaction. AI chapter 2 Reflex agents w/ state AI chapter 2 Goal-based agents AI chapter 2 Utility-based agents AI chapter 2 Mobile agents • Programs that can migrate from one machine to another. • Execute in a platform-independent execution environment. • Require agent execution environment (places). • Mobility not necessary or sufficient condition for agenthood. • Practical but non-functional advantages: • Reduced communication cost (eg, from PDA) • Asynchronous computing (when you are not connected) • Two types: AI chapter 2 Mobile agents • Programs that can migrate from one machine to another. • Execute in a platformindependent execution environment. • Require agent execution environment (places). AI chapter 2 A mail agent Mobile agents • Practical but non-functional advantages: • Reduced communication cost (e.g. from PDA) • Asynchronous computing (when you are not connected) • Two types: AI chapter 2 Mobile agents • Applications: • Distributed information retrieval. • Telecommunication network routing. AI chapter 2 Information agents • Manage the explosive growth of information. • Manipulate or collate information from many distributed sources. • Information agents can be mobile or static. • Examples: • BargainFinder comparison shops among Internet stores for CDs • FIDO the Shopping Doggie (out of service) • Internet Softbot infers which internet facilities (finger, ftp, gopher) to use and when from high-level search requests. AI chapter 2 Summary • Intelligent Agents: • Anything that can be viewed as perceiving its environment through sensors and acting upon that environment through its effectors to maximize progress towards its goals. • PAGE (Percepts, Actions, Goals, Environment) • Described as a Perception (sequence) to Action Mapping: f : P* A • Using look-up-table, closed form, etc. • Agent Types: Reflex, state-based, goalAI chapter 2