ISIRI 13000 Iranian Version of Total Management SystemReza Seifollahy
油
ISIRI 13000 Iranian Version of Total Management System.
This standards covers QMS, EMS, OHSAS and Japanese sustem of 5S and also considered some strategic management and human resource management issues.
This document discusses various search algorithms including best-first search, greedy search, A* search, iterative deepening A* search, recursive best-first search, heuristics, local search algorithms, hill climbing, simulated annealing and genetic algorithms. It provides an overview of each algorithm and notes that uninformed searches only consider the depth of nodes when expanding the search tree, do not take advantage of problem structure, and expand the tree in a predefined manner.
This document discusses artificial intelligence for game playing. It introduces different types of games and optimal strategies for games like minimax and alpha-beta pruning. It also discusses challenges for games of imperfect information that include elements of chance, as well as techniques for heuristic evaluation and expected value calculations when chance is involved.
This document discusses constraint satisfaction problems (CSPs) and techniques for solving them. It begins by defining CSPs as problems with variables, domains of possible values, and constraints limiting assignments. Backtracking search and heuristics like minimum remaining values are described as standard approaches. Constraint propagation techniques like forward checking and arc consistency are explained, which aim to detect inconsistencies earlier. The 4-queens problem is provided as an example CSP.
This document discusses different informed search strategies for artificial intelligence problems. It begins by introducing best-first search and how it selects nodes for expansion based on an evaluation function. A* search is then described, which uses an admissible heuristic function to estimate costs. The document provides an example of running A* search on a problem involving traveling between cities in Romania. It evaluates A* search and discusses variants like iterative-deepening A* and recursive best-first search that aim to reduce its space complexity issues.
This document provides an overview of intelligent agents and artificial intelligence. It discusses agents and environments, defining agents as functions that map percept sequences to actions. It then introduces the vacuum cleaner world as a simple environment with two locations and percepts of the location and cleanliness. The document defines rational agents as those that maximize expected performance based on percepts and prior knowledge. It also categorizes environments based on their observability, determinism, episodic nature, static properties, discreteness, and whether they are single-agent. Finally, it outlines different types of agent programs, including simple reflex agents, model-based reflex agents, goal-based agents, and utility-based agents.
The document discusses problem solving agents and search algorithms. It describes problem solving as having four steps: goal formulation, problem formulation, search, and execution. It then discusses different types of problems agents may face, such as single state problems and problems with partial information. The document introduces tree search algorithms and strategies for searching a state space, such as breadth-first search. It analyzes the performance of breadth-first search and notes its exponential time and memory complexity for large problems.
This document provides an introduction to an Artificial Intelligence course. It outlines practical details like the course homepage and textbook. It then gives an overview of course topics including what AI is, problem solving, planning, learning, and communicating. It also provides a brief history of AI, discussing early work in neural networks and logic programming. It notes differences between Lisp and Scheme programming languages.
This document discusses various search algorithms including best-first search, greedy search, A* search, iterative deepening A* search, recursive best-first search, heuristics, local search algorithms, hill climbing, simulated annealing and genetic algorithms. It provides an overview of each algorithm and notes that uninformed searches only consider the depth of nodes when expanding the search tree, do not take advantage of problem structure, and expand the tree in a predefined manner.
This document discusses artificial intelligence for game playing. It introduces different types of games and optimal strategies for games like minimax and alpha-beta pruning. It also discusses challenges for games of imperfect information that include elements of chance, as well as techniques for heuristic evaluation and expected value calculations when chance is involved.
This document discusses constraint satisfaction problems (CSPs) and techniques for solving them. It begins by defining CSPs as problems with variables, domains of possible values, and constraints limiting assignments. Backtracking search and heuristics like minimum remaining values are described as standard approaches. Constraint propagation techniques like forward checking and arc consistency are explained, which aim to detect inconsistencies earlier. The 4-queens problem is provided as an example CSP.
This document discusses different informed search strategies for artificial intelligence problems. It begins by introducing best-first search and how it selects nodes for expansion based on an evaluation function. A* search is then described, which uses an admissible heuristic function to estimate costs. The document provides an example of running A* search on a problem involving traveling between cities in Romania. It evaluates A* search and discusses variants like iterative-deepening A* and recursive best-first search that aim to reduce its space complexity issues.
This document provides an overview of intelligent agents and artificial intelligence. It discusses agents and environments, defining agents as functions that map percept sequences to actions. It then introduces the vacuum cleaner world as a simple environment with two locations and percepts of the location and cleanliness. The document defines rational agents as those that maximize expected performance based on percepts and prior knowledge. It also categorizes environments based on their observability, determinism, episodic nature, static properties, discreteness, and whether they are single-agent. Finally, it outlines different types of agent programs, including simple reflex agents, model-based reflex agents, goal-based agents, and utility-based agents.
The document discusses problem solving agents and search algorithms. It describes problem solving as having four steps: goal formulation, problem formulation, search, and execution. It then discusses different types of problems agents may face, such as single state problems and problems with partial information. The document introduces tree search algorithms and strategies for searching a state space, such as breadth-first search. It analyzes the performance of breadth-first search and notes its exponential time and memory complexity for large problems.
This document provides an introduction to an Artificial Intelligence course. It outlines practical details like the course homepage and textbook. It then gives an overview of course topics including what AI is, problem solving, planning, learning, and communicating. It also provides a brief history of AI, discussing early work in neural networks and logic programming. It notes differences between Lisp and Scheme programming languages.
4. 鏤o困鏤器鏤o困鏤器鏤o困鏤器鏤o困鏤器
≒鏤鏈鏤o困鏤器惆悋惘悋鏤o鏤わ皿鏤鏤悋悋慍鏈o鏤鏈鏤鏈鏤o参鏈鏈鏈件昏:
鏤鏤 鏤鏤鏤鏤鏤悋悋鏈鏤鏈鏈 鏤o困鏤器惆惘鏤鏈鏤鏈わ鏤鏤鏤鏤惆惘鏤鰹鏤霞悋慍悋鏤鰹殺鏈o鏤鏈鏤鏈鏤o参鏈鏈鏈件昏.
≒鏤o鏈ル:惆鏤э惨鏈ル鏤o鏈金
S = {1, 2, 3, 4, 5, 6, 7, 8}
N. Razavi- AI course- 2005 13
鏤o困鏤器 鏤鏈鏤o鏤o困鏤器 鏤鏈鏤o鏤o困鏤器 鏤鏈鏤o鏤o困鏤器 鏤鏈鏤o
≒惆惘鏤鏈わ鏤鏈件混惺鏤o困鏤器惆惘鏤鰹鏤霞悋慍鏈o鏤鏈鏤鏈ル鏤o擦鏤鏤鏤o参鏈鏈鏈件昏
鏤鏤 鏈鏤鏈 鏈鏈鏤鏤 鏤鏤わ鏤鏈鏤o惆惘鏤o困鏤器鏈鏈鏤鏈鏈鏤鏤器惨鏈鏈o鏤鏈鏤o困鏤器鏤o参鏈件皿惆
≒鏈o鏤鏈鏤鏤鏤鏤霞:Si 鏤霞
≒鏤鏤わ鏤鏈鏤o:Action
≒鏈o鏤鏈鏈鏤鏈:Sj
Si Sj
Action
鏈o鏤鏈鏈鏤鏈:Sj
≒鏈鏈ル:鏈ル 惆鏤р鏤鏈金 ≒鏤o鏈ル:惆鏤э惨鏈ル鏤o鏈金
S CSUCK
N. Razavi- AI course- 2005 14
鏤o困鏤器 悋鏤э皿悋惺鏤o困鏤器 悋鏤э皿悋惺鏤o困鏤器 悋鏤э皿悋惺鏤o困鏤器 悋鏤э皿悋惺
≒鏤鏈鏤o纂鏤鏈鏈鏤鏤o左鏈鏤鏈)惆惘鏤o鏈鏈鏤鏤o左鏈鏤鏈鎰鏈鏤鰹混鏈鏈逸鏤霞(:鏤o困鏤器鏤霞鏤鏤惆惘悛惆惘
鏤鏈鏤鏈わ鏤悋慍慍鏤oル鏈o佐鎬鏈鏤鏈ル鏤鏈鏤o鏈鏤悛悋鏤o鏈ル惆鏈鰹鏤器鏈鏈鏤鏈o鏤鏈鏤鏈鏤o 鏤鏈鏤鏈わ鏤悋慍慍鏤oル鏈o佐鎬鏈鏤鏈ル鏤鏈鏤o鏈鏤悛悋鏤o鏈ル惆鏈鰹鏤器鏈鏤霞鏈鏤鏈o鏤鏈鏤鏈鏤o
鏤o困鏤器惘悋鏤o参惆鏤鏤鏈.
≒鏤o鏈ル:惆鏤э惨鏈ル鏤o鏈金鏈o佐鎬鏈鏤鏈:[location, status] : 鏤癌鏈金鏈:
鏈鏈醐恨鏤器査鏤o鏈ル:鎰種鏤鰹惘悋鏈鰹
鏈鏈醐恨鏈 鏈垂:鏈謂 鏈鏈鏤 鏤鏈
[ , ]
鏈鏈醐恨鏤器査鏈随鏤器:鏈鏤わ惨鏈謂鏤鰹鏤鏈鏤器
[ LEFT, [CLEAN, DIRTY] ]
N. Razavi- AI course- 2005 15
鏤o困鏤器 悋鏤э皿悋惺鏤o困鏤器 悋鏤э皿悋惺鏤o困鏤器 悋鏤э皿悋惺鏤o困鏤器 悋鏤э皿悋惺
≒鏤鏤鏤:)惆惘鏤o鏈鏈鏤悋鏈鏤鏈鏤(:鏈o鏤鏈鏈鏤鏈鏤 鏤o困鏤鏈鏤o纂鏤鏤 鏈鏤鏈鰍鏈o鏤鏈鏤鏤鏤 鏤鏤鏤鏤霞:)惆惘鏤o鏈鏈鏤悋鏈鏤鏈鏤鏤霞(:鏈o鏤鏈鏈鏤鏈鏤o困鏤器鏤鏈鏤o纂鏈鏤鏈鰹惨鏤鏤鏈o鏤鏈鏤鏤鏤鏤霞
鏤鏤わ悋鏤э鏈ル鏈件昏鏈鏤鏈鰹鏤鏈鏤o鏤鏈鏈鏤鏈鏤鏤器惨鏤鏤o参鏈鏈鏈件昏.
鎬鎬鎬悛 悋鎬鏈鏤o困鏤器鏈鏤鏈鏈謂惆惘鏤o皿惘惆鏤鏤わ鏤鏈鏤o鏤鏈ル惆鏤鰹鏈鏤鏤鏤鏤霞鏈鏈鏈件昏悛鏤э鏈ル鏤o困鏤器
悋鏈鰹鏈悋鏈鎬鏤鰹鏤o参鏈鏈鏈件昏.
SUCK
S
S
SUCK
鏤鏤鏤鏤霞
SUCK
悋鏈鏤鏈鏤鏤霞
N. Razavi- AI course- 2005 16
5. 鏤o困鏤器 悋鏤э皿悋惺鏤o困鏤器 悋鏤э皿悋惺鏤o困鏤器 悋鏤э皿悋惺鏤o困鏤器 悋鏤э皿悋惺
≒悋鎰鏤器紺惆鏤鰹)惆惘鏤o鏈鏈鏤鏈鏈鏈鏤器鏤霞(:鏈鏈鏈鏈鏤鏤鏈鏤o鏈鏤損惆惘鏤鏈ル束鏤鏤器混鏤鏈鏈鏤鏈鏈鏈逸山鏤
鏈鏤鏈器惨鏤≒鏤b鏈件皿惆)鏤鏈惆惘鏈件鏤o悋惆惘悋鏤鏈鏤o鏈鰹鏈霞悋鏤э鏈ル鏤鰹鏤鏤わ 鏈鏤鏈器惨鏤≒鏤o参鏈件皿惆)鏤鏈惆惘鏈件鏤o悋惆惘悋鏤鏈鏤o鏈鰹鏈霞悋鏤э鏈ル鏤鰹鏤鏤わ
鏤o参鏈鏈鏈件昏(悋鏤э鏈鏈リ鏤鏤わ惆惘鏤鏈惆惘鏈鏤鏤鏈鏈鏤鏈э皿惆鏤鏤わル惆惘鏈鏈器鎬鏤霞惆悋惘惆.
≒鏤o鏈ル:惘鏈鏈リ鏤鏤鏈鏈鏤鏤鏤鏈鏤鏤器鏤器 惘鏈鏈鏤癌 鏤癌
Episode 1 Episode 2 Episode 3
123
p
234
p
345
p
Accept Reject Accept
N. Razavi- AI course- 2005 17
鏤o困鏤器 悋鏤э皿悋惺鏤o困鏤器 悋鏤э皿悋惺鏤o困鏤器 悋鏤э皿悋惺鏤o困鏤器 悋鏤э皿悋惺
≒悋鏤鰹佐鏈鏈)惆惘鏤o鏈鏈鏤鎰鏤鏤鰹(:鏤o困鏤器惆惘鏈o惨鏤鏈鰹雑鏈鏈金鏤鏈鏤o)鏈鏈悋悋鏤э鏈鏈リ鏤鏤わ(鏈鏤鏤器惨鏈
鏤э擦鏤鏤鏈悋鎬鏈鏈э皿惆鏤o困鏤器鏈鏈鎬鏈鏈件慍鏤oル鏈鏤鏤器惨鏈鏤э鏤鏈鏤鏤o鏤器リ奄鏤鏈リ駅鏤鰍 鏤э擦鏤霞鏤鏤鏈.悋鎬鏈鏈э皿惆鏤o困鏤器鏈鏈鎬鏈鏈件慍鏤oル鏈鏤鏤器惨鏈鏤э鏤鏈鏤鏤霞鏤o鏤器リ奄鏤鏈リ駅鏤鰹参
鏤鏈鏤o鏈鏤鏤器惨鏈鏤鏤鏈悛鏤э鏈ル鏤o困鏤器鏤э惨鏤わ鯖鎰鏤鏤鰹鏤o参鏈鏈鏈件昏.
t t
鏤o困鏤器 鏈鰹雑鏈鏈金
S S 悋鏤鰹佐鏈鏈
S S 鎰鏤鏤鰹
N. Razavi- AI course- 2005 18
鏤o困鏤器 悋鏤э皿悋惺鏤o困鏤器 悋鏤э皿悋惺鏤o困鏤器 悋鏤э皿悋惺鏤o困鏤器 悋鏤э皿悋惺
≒鎬鏈器佐鏈鏤)惆惘鏤o鏈鏈鏤鎰鏤器皿鏈鰹鏤(:鏤o困鏤器鏤霞鏤鏤惆惘悛鏈鏤鏈悋惆鏤o困鏈惆
鏤o鏤わ鏤鰹紺悋慍惆惘鏤鏈鏤鏤わ鏤鏈ル鏤鏈鏤o纂悋鏈随頃鏤鰹 鏈鏤鏈件昏鏈鏈鏈件昏 鏤o鏤わ鏤鰹紺悋慍惆惘鏤鏈鏤鏤わ鏤鏈ル鏤鏈鏤o纂悋鏈随頃鏈鏤鏈鏤鰹鏈件昏鏈鏈鏈件昏.
≒惆惘鏤o困鏤器鎬鏈器佐鏈鏤鏤o鏤わ皿鏤鏤鏈o鏤指鏤o困鏤器鏤鰹鏤o鏤わ皿鏤鏤鎬鏈器佐鏈鏤
鏤b鏈鏈鏈件昏鏈o鏤指鏈鏈器リ鎬鏤鏈鏈鏤鏈鏤わ鏤鰹紺鏤b鏈鏈鏈件雑鏈 鏤o参鏈鏈鏈件昏鏈o鏤指鏈鏈器リ鎬鏤霞鏤鏈鏈鏤鏈鏤わ鏤鰹紺鏤o参鏈鏈鏈件雑鏈.
鏤o鏈ル:鏤o困鏤器惆鏤э惨鏈ル鏤o鏈金
State = {1, 2, , 8}
Action = {Left, Right, Suck, NoOp}
Percept = {[Left, Clean], [Left, Dirty], [Right, Clean], }
N. Razavi- AI course- 2005 19
鏤o困鏤器 悋鏤э皿悋惺鏤o困鏤器 悋鏤э皿悋惺鏤o困鏤器 悋鏤э皿悋惺鏤o困鏤器 悋鏤э皿悋惺
≒鏈鏤鏤鏈鏤o鏤霞)惆惘鏈鏈悋鏈鏈鎰種雑鏈鏤鏈鏤o鏤霞(:鏤鰹鏤鏈鏤o鏈э皿惆愆鏈鏤鏈鏤鏤鏈鏤鰹参惆惘
鏤鏤鏤鏤鏈 鏤o困鏤器鏤鏤わ鏤o参鏤鏤鏈.
鏤o鏈ル:鏤o困鏤器鏤鏈鏤o鏈o鏤鏤鏤鏈鏈鏈鏤鏤鏤わリ鏤o鏤鏈鏤鏤惆鏤э惨鏈ル鏤o鏈金 鏤鏤鏤鏈金
鏤鏤鏤鎬 鏤鏤 ≒鎰種雑鏈鏤鏈鏤o鏤霞:鏈鏤鏈悋惆鏤鏈鏤o鏤鏤鏈鏈鏤鰹鏈鏤鰹鏈惆惘鏈鏤鏈鏤o鏤o参鏈鏈鏈件雑鏈.
鏤o鏈ル:鏈件鏈鏤э)惘鏤鏈鏈鏈鏤霞(惘鏈鏤鏤鏈ル鐘)鏈鏤器殺悋鏤鏤鏈ル鏤鰹鏈鏤器拶鏤鏤わ惨鏈リ悦鏈鏤器殺 :鏈鏈)鏤霞 鏈 惘(拆 惘鏈鏤)鏈鏤器殺 鏤 悋鏤鰹鏤器拶惘 鏤わ惨鏈鏤器殺
悋鏤鏤鏈ル惆鏈鏤器拶惘鏤鏈鏈鏈鏤霞(鏤o困鏤器鏈鏈鏤鏈器参鏈э皿惆鏤鏈リ奄)鏤鏤わ惨鏤器リ悦鏈鏈逸山鏤霞(
N. Razavi- AI course- 2005 20
6. 鏤o困鏤器 悋鏤э皿悋惺鏤o困鏤器 悋鏤э皿悋惺鏤癌 惺 鏤鏤癌 惺 鏤
鏈鏈鏤鏈器参 惘悋鏤э雑鏈鎬鏤霞 鏈鏈 鏈件鏈鏤э
鏈鰹鏤鏈
鏈鰹鏤鏈 鏈鏈 鏈件鏈鏤э
鏈鰹鏤鏈
鏈э惨鏈 鏈鏤鏤 鏈鏤鏤 鏤o左鏈鏤鏈 鏤鏈鏈鏤 鏤鏈鏤o纂
鏤 鏤鏈э惨鏈 悋鏈鰹鏈悋鏈鎬鏤鰹 悋鏈鰹鏈悋鏈鎬鏤鰹 鏤鏤鏤鏤霞
鏈э惨鏈 鏈э惨鏈 鏈э惨鏈 悋 惆惘
鏈э惨鏈 鏈鏤鏤 鎰鏤鏤鰹 鏤э惨鏤わ鯖 悋鏤鰹佐鏈鏈
鏤 鏤 鎬鏈э惨鏈 鏈鏤鏤 鏈鏤鏤 鎬鏈器佐鏈鏤
鏈э惨鏈 鏈э惨鏈 鏈э惨鏈 鏤鏈鏤o鏤霞 鏈鏤
≒鏈鏈鏈件昏 鏤o参 鏤鏈鏤o 鏤鏈悋鏈o参 鏤鏤鏤鏈 鏈鏤鏤器惨鏤 慍鏤鰹リ 鏤o惨鏈椅з 鏈鏤 鏤o困鏤器 鏤э皿惺. 惺
≒悋鏤鏤鏤霞 惆鏤э惨鏈ル:鎰種雑鏈鏤鏈鏤o鏤霞 鎰鏤器皿鏈鰹鏤 鎰鏤鏤鰹 鏈鏈鏈鏤器鏤霞 悋鏈鏤鏈鏤鏤霞 鏈鏈逸鏤霞 鎰鏈鏤鰹混 鏤o左鏈鏤鏈
N. Razavi- AI course- 2005 21
鏤鏈鏤o 鏤鏈ル 鏈鏈鏤э鏤o鯖 鏈鏤悋鏈鏤鏤鏈鏤o 鏤鏈ル 鏈鏈鏤э鏤o鯖 鏈鏤悋鏈鏤鏤鏈鏤o 鏤鏈ル 鏈鏈鏤э鏤o鯖 鏈鏤悋鏈鏤鏤鏈鏤o 鏤鏈ル 鏈鏈鏤э鏤o鯖 鏈鏤悋鏈鏤
≒鏤鰹鏤鏈鏤o鏤鏈鏤o纂鏈鏤鏈鰹惨鏤鏤鏈鏈鏈鏤鏤鏈鏤o鏤o左鏈鏈財鏤o参鏈件皿惆.
悛鏤鎬鏤 鏤鰹リ悛惘:鏈鏈鏈鏤鏤鏈鏤o惆鏤э鏈鏤鏤悋惆惘悋鏤鏤霞惘悋鏈鏤鏤鏤わ鏤э鏈鏈件鏤o参鏤鏤鏈.
≒鏤鰹鏈鏈鏈鏤鏤鏈鏤o)鏤鰹鏤鰹鏤鏤蛇鰍鏤鏤≒悋惘慍鏤鏤鎰種(鏤o雑鏤鏤鏤霞
( i l)(rational)鏤o参鏈鏈鏈件昏.
≒鏤鏈:鏤鰹鏤鏈鏤惘鏈件参鏈鏤鏤o雑鏤鏤惘鎰鏤器リ鏈鰹リ荷鏈鏈鏈鏤鏤鏈鏤o鏤o雑鏤鏤鏤霞鏈鏤鏤鏤惘 鏤
鏤o恨鏈鏈種混鏤o鏤器昏
N. Razavi- AI course- 2005 22
鏈鏈器鏈鏤 鏈鏈 鏈鏈 鏤o鏈鏤 鏤鏈鏤o鏈鏈器鏈鏤 鏈鏈 鏈鏈 鏤o鏈鏤 鏤鏈鏤o鏈鏈器鏈鏤 鏈鏈 鏈鏈 鏤o鏈鏤鏤霞 鏤鏈鏤o鏈鏈器鏈鏤 鏈鏈 鏈鏈 鏤o鏈鏤鏤霞 鏤鏈鏤o
≒鏤鰹惘愆鏈鏤鏤o雑鏤鏤惘鏈鏤鏈誌惨鏤鏈鏈鏈鏤鏤鏈鏤o
≒鏤э左鏈ル鏤鏈鏈 鏈鏤 鏤鏤鏈悋鏈鏤 鏤р悋鏤 悋鏤 ≒鏤э左鏈ル惆鏤鏤鏈鏤鏤鏈鏤鏤器鏤o雑鏈鏈鰹鏈鏈悋鏤鏈惆鏤э鏈鏤鏤悋惆惘悋鏤鏤霞鏤o擦鏤鏤
≒鏤o鏈ル:鏈鏈惆鏤э惨鏈ル鏈鏈リ悦鏈鏈鏤鏤霞Percept Sequence Actionp q
[A, Clean] Right
[A Dirty] Suck[A, Dirty] Suck
[B, Clean] Left
[B Dirty] Suck[B, Dirty] Suck
[A, Clean], [A, Clean] Right
[A Cl ] [A Di t ] S k[A, Clean], [A, Dirty] Suck
N. Razavi- AI course- 2005 23
鏈鏈器鏈鏤 鏈鏈 鏈鏈 鏤o鏈鏤 鏤鏈鏤o 鏈鏈鏤э鏤o鯖鏈鏈器鏈鏤 鏈鏈 鏈鏈 鏤o鏈鏤 鏤鏈鏤o 鏈鏈鏤э鏤o鯖鏈鏈器鏈鏤 鏈鏈 鏈鏈 鏤o鏈鏤鏤霞 鏤鏈鏤o 鏈鏈鏤э鏤o鯖鏈鏈器鏈鏤 鏈鏈 鏈鏈 鏤o鏈鏤鏤霞 鏤鏈鏤o 鏈鏈鏤э鏤o鯖
function TABLE-DRIVEN-AGENT( percept) returns an action
static: percepts, a sequence, initially empty
table, a table of actions, indexed by percept sequence,, , y p p q ,
initially fully specified
append percept to the end of percepts
i LOO ( bl )action LOOKUP( percepts, table)
return action
N. Razavi- AI course- 2005 24
7. 鏈鏈器鏈鏤 鏈鏈 鏈鏈 鏤o鏈鏤 鏤鏈鏤o鏈鏈器鏈鏤 鏈鏈 鏈鏈 鏤o鏈鏤 鏤鏈鏤o鏈鏈器鏈鏤 鏈鏈 鏈鏈 鏤o鏈鏤鏤霞 鏤鏈鏤o鏈鏈器鏈鏤 鏈鏈 鏈鏈 鏤o鏈鏤鏤霞 鏤鏈鏤o
≒鏤o鏈鏤鰹:
15 鏈鏈鏈鏈器惨鏈リ奄鏤鏤鏤器拶)鏤o鏤錫惆惘鏈件鏈鏤э10150鏈鰹鏈(
慍鏤oル鏈鏈器惨鏈リ奄慍鏤鰹リ鏈鏈悋悋鏤鰹鏈リ鏈鏈悋鏈o鏤わル鏈鏈鏤支鏈э鏈 慍惘 鏤癌 鏈慍鏤鰍 鏈鏈鏤鰍 鏈
鏤鏈鏈э皿惆鏤o恨鏈鏈リ悦
鏤鎬鎬 鏈o鏤霞鏈鏈鏤鏈鏈鏤鏤器鏤鰹リ鎬鏤器混鏤э惨鏈リ霞鏈鏤慍鏤oル鏈鏈器惨鏈リ奄慍鏤鰹リ鏈鏈悋鏤鰹リ鎬鏤器混
鏤o昏悋鏈э鏈鏈惆悋惘惆. 鏤
N. Razavi- AI course- 2005 25
鏤鏈 鏤鏈鏤o 悋鏤э皿悋惺鏤鏈 鏤鏈鏤o 悋鏤э皿悋惺鏤鏈 鏤鏈鏤o 悋鏤э皿悋惺鏤鏈 鏤鏈鏤o 悋鏤э皿悋惺
(G li ) ≒鎰種錆鏈リ奄鏤э皿惺悋鏈誌鏤霞鏈鏤鏈鏈鏈鏤器悋鏤鏈椅э山鏈金鏤鏤わ皿鏤o惨鏈(Generality):
鏤鏈鏤o鏤鏈ル悋鏤鏤鏈呉鏈鰹リ(Simple reflex) 鏤鏈鏤o鏤鏈ル悋鏤鏤鏈醐参鏈鰹リ(Simple reflex)
鏤鏈鏤o鏤鏈ル悋鏤鏤鏈醐参鏤o鏈鏤鏤霞鏈鏈鏤o昏(Model-based reflex)
鏤鏈鏤o鏤鏈ル鏤o鏈鏤鏤霞鏈鏈鏤鏈(Goal-based)
鏤鏈鏤o鏤鏈ル鏈鏤 鏤b鏈鏈鰹皿惆鏤o雑鏈(Utility-based) 鏤鏈鏤o鏤鏈ル鏤o鏈鏤鏤霞鏈鏈鏈鰹皿惆鏤o雑鏈(Utility based)
N. Razavi- AI course- 2005 26
鏈鰹リ 悋鏤鏤鏈醐参 鏤鏈ル 鏤鏈鏤o鏈鰹リ 悋鏤鏤鏈醐参 鏤鏈ル 鏤鏈鏤o鏈鰹リ 悋鏤鏤鏈醐参 鏤鏈ル 鏤鏈鏤o鏈鰹リ 悋鏤鏤鏈醐参 鏤鏈ル 鏤鏈鏤o
≒鏈鰹リ鏈鏈鏤鰹殺鏤э皿惺鏤鏈鏤o 鏈鏤鰹殺鏤惺鏤
≒惆惘鏤鏈鏤鏈わ鏤鏤鏤わ鏈鏤鏤鏈鏈鏈悋鏈鰹リ鰍惆惘鏤鏤鏤鏤霞悋鏤э鏈鏈リ鏤o参鏈件皿惆
≒鏤o鏈ル:
function REFLEX-VACCUM-AGENT( [location, status]) returns an action
if Di h S kif status = Dirty then return Suck
else if location = A then return Right
else if location = B then return Left
≒鏈件鏤o鏤鏤悋鏤э惨鏤鏈件混愀-鏤鏤わ鏤o鏤э雑鏈:
else if location B then return Left
鏤鏤器殺 鏤鏈鏤
悋鎬鏈鎰種混悋愃鏈鏈鏤o紺悋鏈鏤鏤o皿鏈鏤器鏈鏤鏤鏤鰹参惘鏈件殺鏈件昏悛鏤э鏈ル鏈鏈鏤o紺鏤鏤
N. Razavi- AI course- 2005 27
鏈鰹リ 悋鏤鏤鏈醐参 鏤鏈ル 鏤鏈鏤o 鏈鰹鏈э鏈リ奄鏈鰹リ 悋鏤鏤鏈醐参 鏤鏈ル 鏤鏈鏤o 鏈鰹鏈э鏈リ奄鏤霞 惘鏤霞 惘
Agent Sensors
E
What the world
is like now
nvironnmen
Wh t ti I
t
What action I
should do now
Condition-action rules
Actuators
N. Razavi- AI course- 2005 28
8. 鏈鰹リ 悋鏤鏤鏈呉 鏤鏈鏤o 鏈鏈鏤э鏤o鯖鏈鰹リ 悋鏤鏤鏈呉 鏤鏈鏤o 鏈鏈鏤э鏤o鯖鏈鰹リ 悋鏤鏤鏈醐参 鏤鏈鏤o 鏈鏈鏤э鏤o鯖鏈鰹リ 悋鏤鏤鏈醐参 鏤鏈鏤o 鏈鏈鏤э鏤o鯖
function SIMPLE-REFLEX-AGENT( percept) returns an action
t ti l t f diti ti lstatic: rules, a set of condition-action rules
state INTERPRET-INPUT( percept)
rule RULE MATCH( state rules)rule RULE-MATCH( state, rules)
action RULE-ACTION[ rule]
return action
N. Razavi- AI course- 2005 29
鏤o昏 鏈鏈 鏤o鏈鏤 悋鏤鏤鏈呉 鏤鏈ル 鏤鏈鏤o)惆悋惘 鏈鏤鏤鏤( 鏤o昏 鏈鏈 鏤o鏈鏤 悋鏤鏤鏈呉 鏤鏈ル 鏤鏈鏤o)惆悋惘 鏈鏤鏤鏤( 鏤o昏 鏈鏈 鏤o鏈鏤鏤霞 悋鏤鏤鏈醐参 鏤鏈ル 鏤鏈鏤o)惆悋惘 鏈o鏤鏤鏤( 鏤o昏 鏈鏈 鏤o鏈鏤鏤霞 悋鏤鏤鏈醐参 鏤鏈ル 鏤鏈鏤o)惆悋惘 鏈o鏤鏤鏤(
≒鏤鏈鏤o悋鏤鏤鏈醐参鏈鰹リ惆惘鏈誌皿惘鏈鏤霞鏤鏈リ奄鏤o参鏤鏤鏈鏤鏤鏤o困鏤器鏤鏈鏤o纂鏤鏈鏈鏤
鏈 鏈醐鏈鏈件昏 鏤o左鏈鏤鏈鏈鏈鏈件昏
≒悋鎬鏈鏤o困鏤器鏤o左鏈鏤鏈鎰鏈鏤鰹混鏈鏈逸鏤霞鏈鏈鏈件昏鎰鏤器鏤器混鏈鏤鏤器惨鏈悋惠惆鏤э惨鏈鏤指荷 鏈鏤癌鏤鰹混 鎰鏤霞 鏈鏈謂鏈鏤器混 鎰鏤癌鏤器惨鏈鏤癌慍
悋鏈鰹
鏤 ≒鏤o鏈ル:鏈鏈鏤鏈器参悋鏈鏤鏤o鏈鏤器
≒鏈鏤鏈為 鏤b惆惺 鏤р惆悋鏤э唆 ≒鏤o佐鏈鏤鏈為惆鏤э皿惺惆悋鏤э唆
鏤э困鏤鏈鏤鏤器惨鏈惆鏤э惨鏈
鏈鏈鏈鏤器混悋鏤鏤わル鏤鏈鏤o鏈鏈惆鏤э惨鏈
N. Razavi- AI course- 2005 30
鏤o昏 鏈鏈 鏤o鏈鏤鏤霞 悋鏤鏤鏈醐参 鏤鏈ル 鏤鏈鏤o鏤o昏 鏈鏈 鏤o鏈鏤鏤霞 悋鏤鏤鏈醐参 鏤鏈ル 鏤鏈鏤o 鏈鏈 鏤霞 鏈 鏤霞 鏤 鏈鏈 鏤霞 鏈 鏤霞 鏤
Sensors
State
E
What the world
is like now
How the world evolves
nviron
What my actions do
nmen
Wh t ti I
t
What action I
should do now
Condition-action rules
Agent Actuators
N. Razavi- AI course- 2005 31
鏤o昏 鏈鏈 鏤o鏈鏤 悋鏤鏤鏈呉 鏤鏈ル 鏤鏈鏤o 鏈鏈鏤э鏤o鯖鏤o昏 鏈鏈 鏤o鏈鏤 悋鏤鏤鏈呉 鏤鏈ル 鏤鏈鏤o 鏈鏈鏤э鏤o鯖鏤o昏 鏈鏈 鏤o鏈鏤鏤霞 悋鏤鏤鏈醐参 鏤鏈ル 鏤鏈鏤o 鏈鏈鏤э鏤o鯖鏤o昏 鏈鏈 鏤o鏈鏤鏤霞 悋鏤鏤鏈醐参 鏤鏈ル 鏤鏈鏤o 鏈鏈鏤э鏤o鯖
function REFLEX-AGENT-WITH-STATE( percept) returns an action( p p )
static: state, a description of the current world state
rules, a set of condition-action rules
action, the most recent action, initially none
state UPDATE-STATE( state, action, percept)
rule RULE-MATCH( state, rules)rule RULE MATCH( state, rules)
action RULE-ACTION[ rule]
return actionreturn action
N. Razavi- AI course- 2005 32
9. 鏤鏈 鏈鏈 鏤o鏈鏤 鏤鏈ル 鏤鏈鏤o鏤鏈 鏈鏈 鏤o鏈鏤 鏤鏈ル 鏤鏈鏤o鏤鏈 鏈鏈 鏤o鏈鏤鏤霞 鏤鏈ル 鏤鏈鏤o鏤鏈 鏈鏈 鏤o鏈鏤鏤霞 鏤鏈ル 鏤鏈鏤o
≒悋鏤鏤種鏈リ鏤指荷鏈鏈悋鏈鏈種擦鏤器拶鎬鏤器混惆惘鏤o皿惘惆鏤鏤わ鏤霞鏤鏤鏈鏈鏤鰹昏悋鏤э鏈ル
鏈件皿惆:鏈件皿惆:
悋鏤鏤種鏈リ鏤o混鏈鏤愀鏈鏤鏈o鏤鏈鏤鏤鏤鏤霞
悋鏤鏤種鏈リ鏤鏈)鏈鏤鏈誌惨鏤鏤o皿鏤鏤鏤器鏤o鏤鏤惡(
鏤o鏈ル:鏤鏤わ鏤o雑鏈鏈鰹鏈鏈悋鏈鏈鏤鏈器参悋鏈鏤鏤o鏈鏤器惆惘鏤鰹鎰種錆鏈リ奄惘悋鏤鏈悋 鏤鏈 鏈鏈鏤霞鏤癌 鏤惘鏤鰍惘 鎰種錆惘
悋鏈鰹)鏈鏈鏤獅鎰鏈鏤鰹惨鏤鎰種惘悋鏈鰹(
≒悋鎬悋 鏈鏈鏈鏤鏤鏈鏈リ霞 鏤р鏈鏤鎰種雑鏈鏤鰹殺鏤 鏤鏈鏈鏈件昏 ≒悋鎬鏈鏈鏈悋惘鏈鰹惨鏈鏈鏤鏤鏈鏤э惨鏈リ霞鏈鏤鎰種雑鏈鏤鰹殺鏤鏤わ鏈鏈鏈件昏
鏈鏈器鏈鏤(search)
鏈鏈鏤э鏤o鯖惘鏤鰹紺(planning)
N. Razavi- AI course- 2005 33
鏤鏈 鏈鏈 鏤o鏈鏤鏤霞 鏤鏈ル 鏤鏈鏤o鏤鏈 鏈鏈 鏤o鏈鏤鏤霞 鏤鏈ル 鏤鏈鏤o鏤霞鏤霞
Sensors
State
E
What the world
is like now
How the world evolves
nviron
What my actions do
What it will be like
nmen
Wh t ti I
if I do action A
t
What action I
should do now
Goals
Agent Actuators
N. Razavi- AI course- 2005 34
鏤o鏈ル鎬鏈悋 鏤鏈 鏤鏈鏤o 鏤o鏈ル鎬鏈悋 鏤鏈 鏤鏈鏤o 鏤o鏈ル:鎬鏈悋 鏤鏈 鏤鏈鏤o 鏤o鏈ル:鎬鏈悋 鏤鏈 鏤鏈鏤o
A
N. Razavi- AI course- 2005 35
鏤o鏈ル:鎬鏈悋 鏤鏈 鏤鏈鏤o 鏤o鏈ル:鎬鏈悋 鏤鏈 鏤鏈鏤o
[UP, UP, UP, RIGHT]
[RIGHT RIGHT RIGHT UP UP UP LEFT LEFT][RIGHT, RIGHT, RIGHT, UP, UP, UP, LEFT, LEFT]
A
N. Razavi- AI course- 2005 36
10. 鏈鰹皿惆鏤o雑鏈 鏤鏈ル 鏤鏈鏤o鏈鰹皿惆鏤o雑鏈 鏤鏈ル 鏤鏈鏤o鏈鰹皿惆鏤o雑鏈 鏤鏈ル 鏤鏈鏤o鏈鰹皿惆鏤o雑鏈 鏤鏈ル 鏤鏈鏤o
≒惆惘鏈鏈器惨鏈リ悦悋慍鏤o困鏤器鏤鏈悋鏤鏈悋鏈鏈悋鏈鏤鏤鏤器昏惘鏤鏈鏈リ悦鏈鏈鏤鏤器鏤器鏈鏈鏤獅鏤o雑鏈鏈鰹
鏤э惨鏈器鏤鏈
≒鏤o鏈ル:鏈鏈鏤鏈器参悋鏈鏤鏤o鏈鏤器
鏤o擦鏤鏤悋鏈鰹鎰種雑鏈鏤鰹殺鏤o佐鏤器混鏈鏈悋惘鏈鰹惨鏈鏈鏤鏤o鏈種昏鏤o皿鏈鏤惆鏈鏈鏈件昏悋鏤o鏈鏤鏤鏤霞悋慍悛鏤э錆鏈 鏤鏤鰹殺 鎰錫鏤器混鏈鏈鏤癌 惘鏈鏤 鏤鏈鏤霞 鏈慍鏤
鏈鰹混鏤鰹鏈鏈悋鏤o殺鏈鏈鏤o鏤わ鏤鏈鏈鏤鰹悋惘慍悋鏤э鏈悋慍鏈鏤鏤器鯖鏤o参鏈鏈鏈件雑鏈
≒悋鏤鏈悋鏤o纂鏤鏤霞鏈эル鏈鏈悋鏈鏤鏈誌惨鏤鏈随鏤器鏤鏈鏤鏈器鏤鏈)鏤o鏤鏤惡鏤э鏤o鏤鏤惡(
≒鏈鏈鏈鏤鏈鰹皿惆鏤o雑鏈:鏈o鏤鏈)鏤鰹惆鏤э鏈鏤鏤悋悋慍鏈o鏤指(惘悋鏈鏤鏤鰹鏤鏈惆鏈o鏤器鏤霞
鏤э鏈鏈件鏤o参鏤鏤鏈鏤鏤惆惘鏈鏤鏤o鏤鏤鏈鏤器悛惘悋鏈鏤鏈誌惨鏤鏤o参鏤鏤鏈
≒悋鏤o鏈ル鏈鏈種擦鏤器拶鎬鏤器混惆惘鏤o皿悋惘惆鏤鏤:
悋鏤鏈悋鏤o鏤鏈鏤鏈鐘鏈鏈鏈件雑鏈 鏈鐘鏈
鎰種雑鏈鏤鰹殺鏤鏈鏈鏤惆惆悋惘惆鏤鏤霞惘鏈鰹惨鏈鏈鏤鏤鏤器獅鏤鰹鏤鏤鏤鏤霞鏤э惨鏈器
N. Razavi- AI course- 2005 37
鏈鰹皿惆鏤o雑鏈 鏈鏈 鏤o鏈鏤鏤霞 鏤鏈ル 鏤鏈鏤o鏈鰹皿惆鏤o雑鏈 鏈鏈 鏤o鏈鏤鏤霞 鏤鏈ル 鏤鏈鏤o 鏤 鏈鏈 鏤霞 鏈 鏤 鏤 鏈鏈 鏤霞 鏈 鏤
Sensors
What the world
Sensors
State
H th ld l
E
What the world
is like now
How the world evolves
What it will be like if
nviron
What my actions do
What it will be like if
I do action A
nmen
Utility
How happy I will be
in such a state
t
What action I
should do now
Agent
Actuators
N. Razavi- AI course- 2005 38
鏤鰹リ鎬鏤器混鏤э昏 鏤鏈ル 鏤鏈鏤o鏤鰹リ鎬鏤器混鏤э昏 鏤鏈ル 鏤鏈鏤o鏤鰹リ鎬鏤器混鏤э昏 鏤鏈ル 鏤鏈鏤o鏤鰹リ鎬鏤器混鏤э昏 鏤鏈ル 鏤鏈鏤o
≒鏈鏤惘鏤鰹雑鎬)1950(:悋鏤鰹昏鏈鏈鏤э鏤o鯖鏤э皿鏤鰹佐鏤霞悋鏤鏤鏤霞鏤鏤鏈件擦鏤鏈鏈鏤鏈誌皿惘惠惆鏈鰹鏤霞
鏤э惨鏈リ霞鏈鏤惘愆鏤鏈ル鏈鰹混鏤鰹鏈鏈 愆
鏈鰹鏈э鏤o鏈件惨鏤鏤鏈ル鏤鰹リ鎬鏤器混鏤э昏悛鏤o皿慍愆鏈鏤悛鏤э錆鏈
≒鏤o皿鏤鏤鏤鏤鏈ル鏤鏈鏤o鏤鰹リ鎬鏤器混鏤э昏
鏤鏤鏈種混鏤鰹リ鎬鏤器混鏤э昏:鏈鏈悋悋鏤鰹鏈リ鏈鏤鏈鏤惆
鏤鏤鏈種混鏤鏈リ駅鏤鰹参:悋鏤э鏈鏈リ鏤鏤鏈鏤鏤器鏤鏈ル鏈эリ縁鏤霞
鏤鏤鏈鏈悋 鏈 悋悛 鏤鏈悋鎬 鏈 鏤o雑鏈鏤鏈:鏈鏤鏤鏤器昏鏈鏈リ駕婚鏤惘惆鏈鏈鏈鏤鏈鏤鏈鏤悋鏈鰹鏈鏤э昏悋惘惆鏤鏈リ駅鏤鰹参鏈鏈悋鏤鏤鏈種混鏤鰹リ鎬鏤器混鏤э昏
鏤o皿鏤鏈鏤o佐鏈鏤鏤:鎰鏤器左鏤鏤鏈リ鏤鏤鏈鏤鏤器鏤鏈ル悋鏤鏈鏈醐鏤鏤霞
≒鏈鏈ル:鏈鏈鏤鏤 鏈鏈 悋鏈 ≒鏤o鏈ル:鏈鏈鏤鏈器参悋鏈鏤鏤o鏈鏤器
鏤鏤鏈種混鏤鏈リ駅鏤鰹参:鏈o混鏤鏈鏈鰹混鏤鰹悋慍鏈э3鏈鏤鏈э1
鏤o雑鏈鏤鏈:惆惘鏤鰹鏤鏈鏈件鏈鏤鰹惘悋鏤э雑鏈鏤鏈ル惆鏤鰹鏈 鏤o雑鏈鏤鏈:惆惘鏤鰹鏤鏈鏈件鏈鏤鰹惘悋鏤э雑鏈鏤鏈ル惆鏤鰹鏈
悋鏤鰹鏈リ鏤鏈鏤э皿鏤э参鏈鏤器鏤э鏈鏈鏈鏈鏤惆悋鏤鰹殺鏤鏤わ悋鏈誌纂忰鏤鏤鏈種混鏤鏈リ駅鏤鰹参
N. Razavi- AI course- 2005 39
鏤鰹リ鎬鏤器混鏤э昏 鏤鏈ル 鏤鏈鏤o鏤鰹リ鎬鏤器混鏤э昏 鏤鏈ル 鏤鏈鏤o鏤鰹リ鎬鏤器混鏤э昏 鏤鏈ル 鏤鏈鏤o鏤鰹リ鎬鏤器混鏤э昏 鏤鏈ル 鏤鏈鏤o
≒悋鏤э皿悋惺惆悋鏤э左鏤霞鏤鏤鏤鏤鏈種混鏤鰹リ鎬鏤器混鏤э昏鏤o参鏈鏤悋鏤э昏鏤鰹リ鏈鎬鏤器混惆:
鎬 鏈鏤悋鏈鏤悋鏤 悋 鏤鰹リ鎬鏤器混鏤o佐鏈鏤鏤器拶悋慍惆鏤э鏈鏤鏤悋惆惘悋鏤鏤霞
鏤鰹リ鎬鏤器混鏤э困鏤鏈鏤鏤器惨鏈悋惠惆鏤э惨鏈:鏤o左鏈鏤鏈惆鏈o鏤鏈鏤o鏤悋鏤鏤霞 鏤器混 鏤鰍鏤鏤器惨鏈鏤癌鏤霞 鏤
鏤鰹リ鎬鏤器混惆惘鏤o皿惘惆鏈鏈鏈鏤器混鏤鏤わ鏤鏈鏤o:鏤o左鏈鏤鏈鏤э鏈鏤鰹鏤鏤鏈鏤鏤器鏤鏈鏤o
鏈ル鏤鏈鏈 ≒鏤o鏈ル:鏤э困鏤鏈鏈鏤o紺鏤鏈惆惆惘鏈鏈リ鏤鏈ル鏈э惨鏈霞
≒鎰鏈リ悋愆鏈鏈鏤鰹擦鏤
N. Razavi- AI course- 2005 40
11. 鏤鰹リ鎬鏤器混 鏤鏈ル 鏤鏈鏤o鏤鰹リ鎬鏤器混 鏤鏈ル 鏤鏈鏤o
P f t d d
S
Performance standard
SensorsCritic
E
feedback
Enviro
Performance
l t
Learning
l t
changes
onmen
elementelement
knowledge
learning
goals
nt
Problem
generator
goals
Agent Actuators
N. Razavi- AI course- 2005 41