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			<h1>
				<a href="/course/15482/f24" rel="bookmark"><span>Autonomous Agents</span>
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			<h2>
				<strong>Course ID</strong>
				15482</h2>
		</h2>

				<strong>Description</strong>
		Autonomous agents use perception, cognition, actuation, and learning to reliably achieve desired goals, where the agents can be smart homes, mobile robots, intelligent factories, self-driving cars, etc. The goal of this course is to introduce students to techniques needed for developing complete, integrated AI-based autonomous agents. Topics include architectures for intelligent agents; autonomous behaviors, perception, and execution; reasoning under uncertainty; optimization; execution monitoring; machine learning; scheduling; and explanation. A focus of the course will be on the integration and testing of autonomous systems to achieve reliable and robust behavior in the face of sensor noise and uncertainty.  The course is project-oriented where small teams of students will design, implement, and evaluate agents that can grow plants autonomously, without human intervention.
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			<strong>Key Topics</strong><br>
			
            agent architectures, finite state machines, error monitoring and testing, explanation of agent behavior, computer vision, optimization and machine learning, resource optimization, scheduling, and task planning, ethics
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			<strong>Required Background Knowledge</strong><br>
			
            a working knowledge of AI or machine learning, strong programming and math skills are also required
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			<strong>Course Relevance</strong><br>
			
            This course serves as a Decision Making and Robotics Cluster menu option for AI majors, Cognition and Reasoning menu option for Robotics, or as an elective for other majors.
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			<strong>Course Goals</strong><br>
			
            Students will have the opportunity to apply their AI/ML skills towards building and deploying a fully functional autonomous agent, and learn how to discover and communicate implementation tradeoffs to stakeholders
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			<strong>Learning Resources</strong><br>
			
            We rely mostly on course lecture slides but the Russell Norvig &quot;AI: A Modern Approach&quot; textbook is available as an optional resource
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			<strong>Assessment Structure
			</strong><br>
			
            30% Individual Assignments (5% each) <br />
30% Group Assignments (10% each) <br />
10% Grow Period Deployments (5% each) <br />
10% Midterm <br />
15% Final Exam <br />
5% Peer Evaluation
      
		</p>
	
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			<strong>Extra Time Commitment</strong><br>
			
            Students will work at times in teams to deploy an autonomous greenhouse. Team meetings and time debugging the hardware will be required, though not every week.
      </p>
	
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			<strong>Course Link</strong><br>
			<a href="http://www.cs.cmu.edu/~15482">http://www.cs.cmu.edu/~15482</a></p>
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				<a href = "/node/15350">Reid Simmons</a><br>
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