پاورپوینت کامل طراحی عامل ها (Design of Agents) 31 اسلاید در PowerPoint

توجه : این فایل به صورت فایل power point (پاور پوینت) ارائه میگردد

 پاورپوینت کامل طراحی عامل ها (Design of Agents) 31 اسلاید در PowerPoint دارای ۳۱ اسلاید می باشد و دارای تنظیمات کامل در PowerPoint می باشد و آماده ارائه یا چاپ است

شما با استفاده ازاین پاورپوینت میتوانید یک ارائه بسیارعالی و با شکوهی داشته باشید و همه حاضرین با اشتیاق به مطالب شما گوش خواهند داد.

لطفا نگران مطالب داخل پاورپوینت نباشید، مطالب داخل اسلاید ها بسیار ساده و قابل درک برای شما می باشد، ما عالی بودن این فایل رو تضمین می کنیم.

توجه : در صورت  مشاهده  بهم ریختگی احتمالی در متون زیر ،دلیل ان کپی کردن این مطالب از داخل فایل می باشد و در فایل اصلی پاورپوینت کامل طراحی عامل ها (Design of Agents) 31 اسلاید در PowerPoint،به هیچ وجه بهم ریختگی وجود ندارد

بخشی از مطالب داخلی اسلاید ها

پاورپوینت کامل طراحی عامل ها (Design of Agents) 31 اسلاید در PowerPoint

اسلاید ۴: Agent typePerceptsActionsGoalsEnvironmentMedical diagnosis systemSymptoms, findings, patients answersQuestions, tests, treatmentsHealthy patients, minimize costsPatient, hospitalSatellite image analysis systemPixels of varying intensity, colorPrint a categorization of sceneCorrect categorizationImages from orbiting satellitePart-picking robot Pixels of varying intensityPick up parts and sort into binsPlace parts in correct binsConveyor belts with partsRefinery controller Temperature, pressure readingsOpen, close valves; adjust temperature Maximize purity, yield, safety Refinery Interactive English tutor Typed words Print exercises, suggestions, correctionsMaximize students score on testSet of students Examples of agents in different types of applications

اسلاید ۵: Examples of agents..Agent TypePerceptsActionsGoalsEnvironmentsBin-Picking RobotImagesGrasp objects; Sort into binsParts in correct binsConveyor beltMedical DiagnosisPatient symptomsTests and treatmentsHealthy patientsPatient & hospitalSoftbotWeb pagesftp, mail, telnetCollect info on a subjectInternet

اسلاید ۶: Definition of ideal rational agentIdeal Rational Agent: For each possible percept sequence, such an agent does whatever action is expected to maximize its performance measure, on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has.What do you think Is this an acceptable definitionNot looking left when crossing the street: If I don’t see a car coming from the left, it is rational to cross the streetBounded rationalityLimited/costly computation timeLimited/costly memory…No. Should also consider taking information gathering actions.

اسلاید ۷: Agent’s strategyAgent’s strategy is a mapping from percept sequence to actionHow to encode an agent’s strategyLong list of what should be done for each possible percept sequence vs. shorter specification (e.g. algorithm)

اسلاید ۸: WARNING: Might not get what you ask for in the performance measure Cleaning robotPick up as much trash as possibleVehicle route optimization Maximize utilizations => Driving fully loadedCapitalizing on oddities in tariff list => RenegotiationDon’t include solution method in the criterion

اسلاید ۹: agent = architecture + program Physical agents vs. software agents (software agents = softbots)This course concentrates on the program

اسلاید ۱۰: function SKELETON-AGENT (percept) returns action static: memory, the agent’s memory of the world memory UPDATE-MEMORY(memory,percept) action CHOOSE-BEST-ACTION(memory) memory UPDATE-MEMORY(memory, action) return actionOn each invocation, the agent’s memory is updated to reflect the new percept, the best action is chosen, and the fact that the action was taken is also stored in the memory. The memory persists from one invocation to the next.Skeleton agentInput = Percept, not historyNOTE: Performance measure is not part of the agent

اسلاید ۱۱: Examples of how the agent function can be implemented Table-driven agentSimple reflex agentReflex agent with internal stateAgent with explicit goalsUtility-based agentMore sophisticated

اسلاید ۱۲: function TABLE-DRIVEN-AGENT (percept) returns action static: percepts, a sequence, initially empty table, a table, indexed by percept sequences, initially fully specified append percept to the end of percepts action LOOKUP(percepts, table) return actionAn agent based on a prespecified lookup table. It keeps track of percept sequence and just lo