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Task Description: SUMMARY - week-two - lecture 1-4 - ]

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Content: use HTML Edit Content <h1 style="text-align: center;">CREATE-article# #</h1> <h2>[previously]</h2> <ol> <li><strong>[2017-01-dd][00:00] NEW task IN ?</strong></li> <ol> <li>] # 6830 - CREATE-article# #&nbsp;</li> </ol> <li><strong>[00:00] NEW article IN&nbsp;</strong></li> <ol> <li>] # # - week-two</li> </ol> <li><strong>[00:00] NEW task IN strategy(tm)</strong></li> <ol> <li>] read the PDF (slides +)</li> <li>] quick video - ?] SUMM notes</li> </ol> <li>]&nbsp;</li> </ol> <h2>[currently]</h2> <ol> <li><strong>[AGENDA-week-two]</strong></li> <li><strong>] intelligent agents and uninformed search&nbsp;</strong></li> <ol> <li>] intelligent agents&nbsp;</li> <li>] search agents</li> <li>] uninformed search</li> <li>] uninformed search examples</li> </ol> <li><strong>] week 2 quiz</strong></li> <ol> <li>] &nbsp;</li> </ol> <li><strong>] week 2 project search algorithms</strong></li> <ol> <li>] PROJECT -&nbsp;</li> </ol> <li><strong>] week 2 discussion questions</strong></li> <ol> <li>] Q1</li> </ol></ol> <div><hr /></div> <ol> <li><strong>[12:55] VIDEO intelligent agents</strong></li> <ol> <li>] different aspects related to rational agents&nbsp;</li> <li>] define:agent - anything that can be viewed as percieving its environment(through sensors) and acting upon that environment through actuators</li> </ol> <li>] an agent program runs in cycles of ] percieve, ] think/deliberate/decide ] act</li> <li>] made of two main components, - ] Architectural(physical), ] Program</li> <li>] examples agents =&nbsp;</li> <ol> <li>] human - sensors = eyes, ears, other organs, &nbsp;</li> <li>] robots - sensors = cameras, infrared range finders,&nbsp;</li> <li>] household - thermostat, cell phone, robot, roomba, amazon echo, self driving car(SDC), ] human</li> <li>] example echo &nbsp;- maintain shopping list, order, order pizza, order uber, play music, ...</li> <li>] example SDC - percieves its enviro, other as pedestrians, traffic, roads, other cars&nbsp;</li> <li>] example roomba - can percieve clean/dirty, can move left/right or no, suck or no suck</li> </ol> <li>] percept/action table pairs &nbsp; &nbsp;dirty | suck&nbsp;</li> <li>] sequence of percepts, &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;</li> <li><strong>[06:30] well behaved agents </strong>- for each possible percept sequence, select an action that is expected to maximize its performance measure</li> <li>] rationality is relative to performance measure</li> <li>] agent prior knowledge of environment</li> <li><strong>[06:45] PEAS - acronym when defining rational agent</strong></li> <li>] performance -&nbsp;</li> <li>] environment &nbsp;-&nbsp;</li> <li>] actuators &nbsp;-</li> <li>] sensors - &nbsp;</li> <li><strong>[07:00] define PEAS for SDC&nbsp;</strong></li> <li>] performance = safety, time to dest, legal drive, comfort,</li> <li>] environment = roads, other cars, pedestrians, road signs</li> <li>] actuators = steering, accelorator, brake, horn</li> <li>] sensors = camera, sonar, gps, speedomoter, odomoter, accelerometer, engine sensors, keyboards</li> <li><strong>[08:00] define PEAS for roomba&nbsp;</strong></li> <li>] performance = cleanliness, efficiency, distance travelled, battery life, security</li> <li>] environment = room, table , wood floors, carpet, different obstacles,&nbsp;</li> <li>] actuators = wheels, different brushes, vacuum extractor</li> <li>] sensors = camera, dirt detectory, cliff sensor(stairs), bump sensor(furniture), infrared wall sensor&nbsp;</li> <li><strong>[10:00] environment types&nbsp;</strong></li> <li>] fully observable VS partially observable - agents sensors give it access to the complete state of the environment</li> <li>] deterministic VS stochastic -&nbsp;</li> <li>] episodic VS sequential -&nbsp;</li> <li>] static VS dynamic -&nbsp;</li> <li>] discrete VS continuos -&nbsp;</li> <li>] single agent vs multi agent -&nbsp;</li> <li>] known vs unknown -&nbsp;</li> </ol> <h2>[next]</h2> <ol> <li>] NEW task&nbsp;</li> <ol> <li>] preview week 2 project -</li> </ol></ol> <h2>[reference]</h2> <div><ol> <li>] PDF week-002, loc=desktop</li> <li>] + RESEARCH note =&nbsp;https://www.quora.com/What-is-the-best-online-course-to-learn-AI</li> <li>] week-two project <a href="https://courses.edx.org/courses/course-v1:ColumbiaX+CSMM.101x+1T2017/discussion/forum/7c1981890b9c32450921c4a2984f9284876ac2b5/threads/58857bd87f6e4407bc001bcd" target="_blank">discussion</a> - ] py 2 or 3 ?&nbsp;</li> <li>] g?='breadth first search' - WHAT = &nbsp;based on Q data structure, WHEN searching a graph, HOW search each node at same level, then down 1 level,&nbsp;</li> <li>] r# 1.1 = &nbsp;]&nbsp;https://en.wikipedia.org/wiki/Breadth-first_search&nbsp;</li> <li>] r# 1.3 = ][05:00][VIDEO]&nbsp;<a href="https://www.youtube.com/watch?v=QRq6p9s8NVg" target="_blank">Breadth First Search Algorithm</a>&nbsp;- 500k, time complexity =&nbsp;</li> <li>] g?=python breadth first search</li> <li>] r# 1.# =&nbsp;https://interactivepython.org/courselib/static/pythonds/Graphs/ImplementingBreadthFirstSearch.html ( ] SITE simple clean struct for book )&nbsp;</li> </ol></div>