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蒙纳什大学马来西亚校区 / 课程

FIT5222

Planning and automated reasoning

6 credit pointsLevel 5First semester / Second semesterMalaysiaFaculty of Information Technology

基本信息

学分6 credit points
开课学期First semester / Second semester
校区Malaysia
考核构成
Assignment 138%
Assignment 1: Flatland Challenge32%
In-semester test: Heuristic Search18%
In-semester test: Automated Planning18%
Assignment 238%
Lab Reports24%
Assignment 2: Overcooked32%

开课安排2

教学期授课方式状态
First semesterTeaching activities are on-campus (ON-CAMPUS)开课
Second semesterTeaching activities are on-campus (ON-CAMPUS)开课

以上为该校区在官方资料中登记的全部开课安排,不是汇总。同一门课可能在多个 教学期开课,也可能不同教学期的授课方式不同。

课程简介

This unit focuses on the foundations of automated planning and reasoning and their real-world applications. Autonomous agents are active agents that independently execute actions to achieve a certain goal or goals. These agents perceive their environment and reason and plan in order to effect their environment and achieve their goals. This is a very popular and highly researched AI approach and has many significant implications beyond the traditional area of AI (optimisation, robotics, scheduling, etc?). This course will give you the foundations to develop and design your own autonomous agents.

以上为 Monash Handbook 的官方原文,版权属 Monash University,此处按本站要求转载并标注出处: 官方页面 ↗

学习成果6

官方原文(Learning outcomes),版权属 Monash University。

  1. ULO1 explain the theoretical concepts of automated planning and reasoning techniques;
  2. ULO2 apply agent modelling techniques to analyse, design and implement a small agent-based system;
  3. ULO3 evaluate, design, and implement automated planning and reasoning technique;
  4. ULO4 describe strengths and weaknesses of different automated planning and reasoning approaches for software agents;
  5. ULO5 apply automated planning and concurrent programming techniques to non-trivial distributed problems;
  6. ULO6 describe and discuss planning security challenges and solutions through goal recognition algorithms.

教学方式与预期工作量

教学方式

Active learning

预期工作量

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled teaching activities.

官方原文,版权属 Monash University。

先修 / 同修要求

以下先修关系按官方来源的结构化先修字段解析,原始记号:FIT9136 AND MAT9004

FIT9136Introduction to Python programming
MAT9004Mathematical foundations for data science and AI

先修链路

按官方先修字段的原始分组展开,AND / OR 的区别保留着—— 「A 或 B」和「A 与 B」在选课时是两回事。每门课点进去可以继续往下看。

以下全部都要
FIT9136Introduction to Python programming6 cp
MAT9004Mathematical foundations for data science and AI6 cp

属于这些学位1

这门课出现在下列学位的官方结构里。反过来说:如果你读的是这些学位之一,它大概率是要修的 (必修还是选修取决于它在 Part 里的位置,点进去看结构)。

数据来源

数据来源
官方网页
handbook.monash.edu
抓取时间
2026-09-13
可信度
程序抓取,未人工核实

查看官方完整描述 ↗ — 事实性字段(代码、学分、教学期、授课方式、考核权重、先修/同修/互斥关系)与 课程简介、学习成果、教学方式、预期工作量均取自官方 Handbook; 正文版权属 Monash University,此处转载并逐处标注出处。

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