Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Agents of S.H.I.E.L.D. (season 4) wikipedia , lookup
Embodied language processing wikipedia , lookup
History of artificial intelligence wikipedia , lookup
Soar (cognitive architecture) wikipedia , lookup
Agent-based model wikipedia , lookup
Reinforcement learning wikipedia , lookup
Agent-based model in biology wikipedia , lookup
Agent (The Matrix) wikipedia , lookup
AI in game (I) 권태경 Fall, 2006 outline • AI definition taxonomy • agents What is AI? Views of AI fall into four categories: Thinking humanly Thinking rationally Acting humanly Acting rationally The textbook advocates "acting rationally" 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 • Thinking humanly: cognitive modeling • Need to get inside the actual workings of human minds • • Goal – Program’s I/O and timing behaviors match corresponding human behaviors – • How to validate? It requires 1) Predicting and testing behavior of human subjects (top-down) or 2) Direct identification from neurological data (bottom-up) • Real “cognitive science” should experiment actual humans or animals • Thinking rationally: "laws of thought" • Aristotle: what are correct arguments/thought processes? • – • • • Syllogism Direct line through mathematics and philosophy to modern AI Problems: 1. Informal knowledge, e.g. grey zone, certainty 2. Solving a problem in principle vs. doing so in practice 3. Acting rationally: rational agent • A rational agent is one that acts so as to achieve the best outcome or when there is uncertainty, the best expected outcome – E.g. autonomous control, adapt to change – • Correct inference cannot do the job sometimes – Acting rationally does not necessarily involve inference – E.g. a reflex action – • We need learning – Understanding howprinciples the world of works helpsagents to generate Focus on general rational more effective strategies to deal with it and on components for constructing them – Rational agents • An agent is an entity that perceives and acts • • 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 • Percept: the agent’s perceptual inputs at any given instant • Caveat: computational limitations make perfect outline • AI taxonomy • agent 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 Agents and environments • The agent function: an abstract mathematical description – The agent function maps from percept histories to actions: – [f: P* A] • The agent program: a concrete implementation – The agent program runs on the physical architecture to implement f Vacuum-cleaner world • Percepts: location and contents, e.g., [A,Dirty] • • Actions: Left, Right, Suck, NoOp • Rational agents: definition • 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. • • 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. Rational agents: issues • Rationality is distinct from omniscience (allknowing with infinite knowledge) • • Agents can perform actions in order to obtain useful information or to modify future percepts (information gathering, exploration) – Not for performance maximization • An agent is autonomous if its behavior is determined by its own experience (with ability to learn and adapt) • – Become independent of the prior knowledge from its PEAS: formalization • 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 PEAS • 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 – 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 – In partially observable case, it could appear to be stochastic – 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 – the choice of action in each episode depends only on the episode itself 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 – Entity B is an agent or merely a stochastically behaving object? • Maximize its performance measure depending on agent A’s behavior – Multiagent • competitive vs. cooperative • • Communication • A hardest combination from 6 categories? Environment types Fully observable Deterministic Episodic Static Discrete Single agent Chess with a clock Yes Strategic No Semi Yes No Chess without a clock Yes Strategic No Yes Yes No Taxi driving No No No 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 • 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 • Table-lookup agent function Table-Driven-Agent(percept) returns an action static: percepts, a sequence // initially empty table, a table of actions // indexed by percept sequences append percept to the end of percepts action <- LookUp (percepts, table) return action • 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 Agent types • Four basic types in order of increasing generality: • – Simple reflex agents – Model-based reflex agents – Goal-based agents – Utility-based agents • How to convert into learning agents Simple reflex agents Simple reflex agents function Simple-Reflex-Agent(percept) returns an action static: rules, a set of condition-action rules state <- Interrupt-Input (percept) rule <- Rule-Match (state,rules) action <- Rule-Action (rule) return action • A condition-action rule – If (condition) then (do a specific action) • Interrupt-Input: abstract description of the current state • Agent’ intelligence is limited – It works well when environments are fully observable – If partially observable, some problem, e.g. infinite loop, can occur – Randomized action can escape from infinite loops Model-based reflex agents Model-based reflex agents function Model-Based-Reflex-Agent(percept) returns an action static: state, a description of the current world rules, a set of condition-action rules action, the most recent action state <- Update-State (state, action, percept) rule <- Rule-Match (state, rules) action <- Rule-Action (rule) return action • To handle partial observability, agent keeps track of the part of the world it cannot see now – Internal state • Tries to model the world in two ways – How the world evolves independently of the agent – How the agent’s action affect the world (model-based) Goal-based agents • • • • The agent needs goal information that describes desirable situations Consider future Search and planning to find action sequences for goal Less efficient but more flexible Utility-based agents • Goals alone are not enough to generate high-quality behavior sometimes – Goal are often binary distinction e.g. happy vs. unhappy • A utility function maps a state (or its sequence) onto a real number, e.g. the degree of happiness – Can provide a tradeoff between conflicting goals e.g. speed vs. security • If multiple goals, the likelihood of success of each goal can be weighed up against the importance of the goals Utility-based agents Learning agents • So far, we talked about various methods for selecting actions – We have not explained how the agent programs come into being – • 4 major components – Learning element is responsible for making improvements • Percept has no idea of how to evaluate the state of the world • Uses feedback from critic – Critic tells the learning agent how well the agent is doing • in terms of performance standard • Note that performance standard is fixed – Performance element is responsible for selecting external actions • This is the agent in the previous slides • Takes percept and decides on actions – Problem generator is responsible for suggesting actions that will lead to new and informative experiences Learning agents reference • Artificial Intelligence: A Modern Approach (Second Edition) by Stuart Russell and Peter Norvig, Prentice Hall Potential project ideas • Realty problem • Education or Entrance exam problem