[Virtual Presenter] Welcome to the lecture of Module 1 of the Introduction to Artificial Intelligence course.. 

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COM571 Introduction to Artificial Intelligence Saint Leo University College of Computing, Artificial Intelligence, Robotics, and Data Science (CARDS)

[Audio] Demonstrate a fundamental understanding of the history of artificial intelligence (AI) and its foundations.. 

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Demonstrate a fundamental understanding of the history of artificial intelligence (AI) and its foundations.

[Audio] What is Artificial Intelligence?
History of Artificial Intelligence
Agents Situated in Environments
Designing Intelligent Agents
Prototypical Applications. 

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What is Artificial Intelligence? History of Artificial Intelligence Agents Situated in Environments Designing Intelligent Agents Prototypical Applications

[Audio] Artificial intelligence (AI) is a broad field of science and technology that involves building machines and computers to perform tasks that typically require human intelligence.. 

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Artificial intelligence is the synthesis and analysis of computational agents that act intelligently. An agent is something that acts in an environment. An agent acts intelligently if: its actions are appropriate for its goals and circumstances it is flexible to changing environments and goals it learns from experience it makes appropriate choices given perceptual and computational limitations

[Audio] An AI agent is a software program that uses artificial intelligence (AI) to perform tasks without human intervention.
AI agents use machine learning and natural language processing (NLP) to understand and respond to user inputs. They can also use large language models (LLMs) to comprehend and respond to user inputs. AI agents can be used in many applications, including Contact centers, Software design, IT automation, Code-generation tools, and Conversational assistants.. 

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An AI agent is a software program that uses artificial intelligence (AI) to perform tasks without human intervention.

[Audio] In the context of Artificial Intelligence, "dimensions of complexity" refer to different aspects or factors that contribute to the overall complexity of an AI system, often including elements like the number of agents involved, the level of uncertainty in the environment, the type of data being processed, the complexity of the task, and the dynamic nature of the situation, allowing researchers to analyze and categorize AI problems based on their varying levels of difficulty across these dimensions.. 

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Dimension Values Modularity flat, modular, hierarchical Planning horizon non-planning, finite stage, indefinite stage, infinite stage Representation states, features, relations Computational limits perfect rationality, bounded rationality Learning knowledge is given, knowledge is learned Sensing uncertainty fully observable, partially observable Effect uncertainty deterministic, stochastic Preference goals, complex preferences Number of agents single agent, adversary, multiple agents Interaction offline, online

[Audio] This lecture discussed the importance of representations in solving problems, defining solutions, quality of solutions, decision-making, representation languages, physical symbol systems, knowledge levels, and reasoning in artificial intelligence.. 

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This lecture discussed the importance of representations in solving problems, defining solutions, quality of solutions, decision-making, representation languages, physical symbol systems, knowledge levels, and reasoning in artificial intelligence.

Lecture Module 1

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Demonstrate a fundamental understanding of the history of artificial intelligence (A-I---) and its foundations.

What is Artificial Intelligence?
History of Artificial Intelligence
Agents Situated in Environments
Designing Intelligent Agents
Prototypical Applications

Artificial intelligence (A-I---) is a broad field of science and technology that involves building machines and computers to perform tasks that typically require human intelligence.

An (A-I ) agent is a software program that uses artificial intelligence (A-I---) to perform tasks without human intervention.
(A-I ) agents use machine learning and natural language processing (N-L-P--) to understand and respond to user inputs. They can also use large language models (LLMs) to comprehend and respond to user inputs. 
(A-I ) agents can be used in many applications, including Contact centers, Software design, (I-T ) automation, Code generation tools, and Conversational assistants.

In the context of Artificial Intelligence, "dimensions of complexity" refer to different aspects or factors that contribute to the overall complexity of an (A-I ) system, often including elements like the number of agents involved, the level of uncertainty in the environment, the type of data being processed, the complexity of the task, and the dynamic nature of the situation, allowing researchers to analyze and categorize (A-I ) problems based on their varying levels of difficulty across these dimensions.

This lecture discussed the importance of representations in solving problems, defining solutions, quality of solutions, decision making, representation languages, physical symbol systems, knowledge levels, and reasoning in artificial intelligence.