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] different aspects related to rational agents
] define:agent - anything that can be viewed as percieving its environment(through sensors) and acting upon that environment through actuators

] an agent program runs in cycles of ] percieve, ] think/deliberate/decide ] act
] made of two main components, - ] Architectural(physical), ] Program
] examples agents =

] human - sensors = eyes, ears, other organs,
] robots - sensors = cameras, infrared range finders,
] household - thermostat, cell phone, robot, roomba, amazon echo, self driving car(SDC), ] human
] example echo - maintain shopping list, order, order pizza, order uber, play music, ...
] example SDC - percieves its enviro, other as pedestrians, traffic, roads, other cars
] example roomba - can percieve clean/dirty, can move left/right or no, suck or no suck

] percept/action table pairs dirty | suck
] sequence of percepts,
[06:30] well behaved agents - for each possible percept sequence, select an action that is expected to maximize its performance measure
] rationality is relative to performance measure
] agent prior knowledge of environment
[06:45] PEAS - acronym when defining rational agent
] performance -
] environment -
] actuators -
] sensors -
[07:00] define PEAS for SDC
] performance = safety, time to dest, legal drive, comfort,
] environment = roads, other cars, pedestrians, road signs
] actuators = steering, accelorator, brake, horn
] sensors = camera, sonar, gps, speedomoter, odomoter, accelerometer, engine sensors, keyboards
[08:00] define PEAS for roomba
] performance = cleanliness, efficiency, distance travelled, battery life, security
] environment = room, table , wood floors, carpet, different obstacles,
] actuators = wheels, different brushes, vacuum extractor
] sensors = camera, dirt detectory, cliff sensor(stairs), bump sensor(furniture), infrared wall sensor
[10:00] environment types
] fully observable VS partially observable - agents sensors give it access to the complete state of the environment
] deterministic VS stochastic -
] episodic VS sequential -
] static VS dynamic -
] discrete VS continuos -
] single agent vs multi agent -
] known vs unknown -

] PDF week-002, loc=desktop
] + RESEARCH note = https://www.quora.com/What-is-the-best-online-course-to-learn-AI
] week-two project discussion - ] py 2 or 3 ?
] g?='breadth first search' - WHAT = based on Q data structure, WHEN searching a graph, HOW search each node at same level, then down 1 level,
] r# 1.1 = ] https://en.wikipedia.org/wiki/Breadth-first_search
] r# 1.3 = ][05:00][VIDEO] Breadth First Search Algorithm - 500k, time complexity =
] g?=python breadth first search
] r# 1.# = https://interactivepython.org/courselib/static/pythonds/Graphs/ImplementingBreadthFirstSearch.html ( ] SITE simple clean struct for book )

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