Chapter 2 - MITI 5113 ARTIFICIAL INTELLIGENCE Lecture 2...

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MITI 5113 ARTIFICIAL INTELLIGENCE Lecture 2 - Intelligent Lecture 2 - Intelligent Agents Agents 04/16/17 1
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Outline: Definition of what is an agent Architectures for Agents Applications of Agents Some observations 04/16/17 2
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“An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through effectors ,” (from Russell and Norvig, Artificial Intelligence: a Modern Approach, 1995 Prentice-Hall) If we define the environment as whatever provides input and receives output, take input to be sensing, and producing output to be acting then every program is an agent. 04/16/17 3
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A weak notion (from Woodridge and Jennings, Queen Mary & Westfield College University of London): autonomy: agents operate without direct intervention of humans, and have control over their actions and internal state; social ability: agents interact with other agents (and possibly humans) via an agent communication language; reactivity: agents perceive their environment and respond in a timely and rational fashion to changes that occur in it; pro-activeness: agents do not simply act in response to their environment, they are capable of taking the initiative (generate their own goals and act to achieve them). 04/16/17 4
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A stronger notion (from Woodridge and Jennings): An agent has mental properties, such as knowledge , belief, intention, obligation . In addition, and agent has other properties such as: mobility: agents can move around from one machine to another and across different system architectures and platforms; veracity: agents do not knowingly communicate false information; benevolence: agents always try to do what they are asked of; rationality: agents will try to achieve their goals and not act in such a way to prevent their goals from being achieved. 04/16/17 5
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One last definition: Intelligent agents are software entities that carry out some set of operations on behalf of a user or another program with some degree of independence or autonomy, and in so doing, employ some knowledge or representation of the user’s goals or desires (from IBM). 04/16/17 6
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From Russell and Norvig, “Artificial Intelligence: a Modern Approach”, Chapter 2. Agents and environments Rationality PEAS (Performance measure, Environment, Actuators, Sensors) Environment types Agent types 04/16/17 7
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Agents include humans, robots, softbots, thermostats, etc.
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