Learning From Observation - Artificial Intelligence - Unit-V

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Information Learning From Observation - Artificial Intelligence - Unit-V

Title	:	Learning From Observation - Artificial Intelligence - Unit-V
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Good Explanation mam, keep doing
Comment from : @deptofcsekec8410

AI material plz send mam aksanaga11@gmailcom
Comment from : @achsahmaddela3811

Plz send notes mambraksanaga11@gmailcom not only this topic whole material plz send mam
Comment from : @achsahmaddela3811

Components of Learning Agents:br● A direct mapping from conditions on the current state of actionsbr●A means to infer relevant properties of the world from percept sequencebr●Information aboutthe way the world evolves and about the results of possible actions the agent can takenbr●Utility info indicating the desirability of world statesbr●Action value info indicating the desirability of actionsbr●Goals that describe classes of states whose achievement maximises the agent utilitybr●●●
Comment from : @aswanivijayan5899

1)List the components of Learning agent? brAns) • A direct mapping from the conditions on the current state of action br• A means to infer relevant properties of the world from the percept sequence br• Information about the way the world evolves and about the results of possible actions the agent can take br• Utility information indicating the desirability of world states br• Action value information indicating the desirability of actions br•Goals that describes classes of states whose achievement maximizes the agent utility
Comment from : @jesnajoseph8670

1)components of learning agent:br ☆A direct mapping from conditions on the current state to action br☆A means to infer relevant properties of the world from the percept sequence br☆Information about the way the world evolves and about the result of possible actions the agent can takebr☆Utility information indicating the desirability of world statesbr☆Action value information indicating the desirability of actions br☆Goals that describe classes of states whose achievement maximize the agent's utility brbr2)Supervised learning :The algorithm learns and labeled data set, providing an answer key that the algorithm can use to evaluate it's accuracy on training brUnsupervised learning : provides unlabeled data The algorithm tries to make sense by extracting features and patterns on it's own brbrReinforcement learning :It is a type of dynamic programming that trains algorithm using a system of reward and punishment
Comment from : @madhavimaram2293

1 List the components of Learning agent? brA A direct Mapping from the conditions on the current state to action br* A means to infer relevant properties of the world from the percept sequence br* Information about the way the world evolves and about the results of possible actions the agent can take br* Utility information indicating the desirability of world states br* Action value information indicating the desirability of actions br* Goals that describes classes of states whose achievement maximizes the agent utility br br br2 Compare Supervised, Unsupervised and Reinforcement Learning? brA Supervised Learning technique deals with the labelled data br* Supervised learning agent has high complexity br* Supervised Learning can also conduct offline analysis br* The outcome of supervised learning is more accurate and reliable br br* Unsupervised learning works with unlabelled data br* Unsupervised learning agent has low complexity br* Unsupervised learning employs real-time analysis br* Unsupervised learning generates moderate but reliable results br br* Reinforcement Learning has different tasks such as exploration or exploitation br* In the model "Markov's Decision Process" basic reinforcement is defined br* In this learning, the system or learning agent itself creates data on its own by interacting with the environment br* Reinforcement Learning is preferred in the area of Artificial Intelligence
Comment from : @revathins4167

b*/b List the components of learning agentbrAns: Components of learning agents:br -->A Direct mapping from conditions on the current state to actions br-->A means to infer Relevant properties of the world from the percept sequence br--> Information about the way the world evolves and about the results of possible actions the agent can take br-->Utility information indicating the desirability of world states br-->Action-value information indicating the desirability of actions br-->Goals that describe classes of states whose achievement maximizes the agents utility
Comment from : @msaipriya9690

Components of learning agents:br*A direct mapping from conditions on the current state to actionsbr*A means to infer relevant properties of the world from the percept sequencebr*Information about the way the world evolves and about the results of possible actions the agent can takebr*Utility information indicating the desirability of world statesbr*Action-value information indicating the desirability of actionsbr*Goals that describe classes of states whose achievement maximizes the agent's utility
Comment from : @aishwaryash2224

Q1 List the components of Learning agent?brA A direct Mapping from the conditions on the current state to actionbr* A means to infer relavent properties of the world from the percept sequencebr* Information about the way the world evolves and about the results of possible actions the agent can takebr* Utility information indicating the desirability of world statesbr* Action value information indicating the desirability of actionsbr* Goals that describes classes of states whose achievement maximizes the agent utilitybrbrbrQ2 Compare Supervised, Unsupervised and Reinforcement Learning?brA Supervised Learning technique deals with the labelled databr* Supervised learning agent has high complexitybr* Supervised Learning can also conduct offline analysisbr* The outcome of supervised learning is more accurate and reliablebrbr* Unsupervised learning works with unlabelled databr* Unsupervised learning agent has low complexitybr* Unsupervised learning employs real-time analysisbr* Unsupervised learning generates moderate but reliable resultsbrbr* Reinforcement Learning has different tasks such as exploration or exploitationbr* In the model "Markov's Decision Process" basic reinforcement is definedbr* In this learning, the system or learning agent itself creates data on its own by interacting with the environmentbr* Reinforcement Learning is preferred in the area of Artificial Intelligence
Comment from : @ramcharanreddy7665

Q: List the components of learning agentbrAns: Components of learning agents:br -->A Direct mapping from conditions on the current state to actions br-->A means to infer Relevant properties of the world from the percept sequence br--> Information about the way the world evolves and about the results of possible actions the agent can take br-->Utility information indicating the desirability of world states br-->Action-value information indicating the desirability of actions br-->Goals that describe classes of states whose achievement maximizes the agents utility
Comment from : @amruthadabbara8959

bComponents of learning agents/bbr<>DIRECT MAPPING from conditions on the current statement of actionbr<> a means to infer irrelevant properties of the world from the percept sequencebr<>Information about the way the world evolves and about the result of possible actions that the agent can takebr<>UTILITY INFORMATION indicates the desirable of world Statesbr<>ACTION VALUE INFORMATION indicates the desirability of actionsbr<>GOALS the describe classes of states whose achievement maximize the agents utility
Comment from : @purushothamk5053