Learning, Goal-based agents are AI systems designed to achieve specific objectives by planning a sequence of actions and adapting to environmental changes to reach their goals. They differ from simpler agents by focusing on future outcomes rather than just immediate reactions, utilizing search and planning algorithms to find the most efficient path to a desired state. Examples include GPS navigation systems and automated logistics systems, which demonstrate the ability to consider a destination and plan routes accordingly.
#goalbasedagent
#agoalBasedAgentinmachinelearning
#goalbasedartificialagent
#goalbasedagentsinartificialagent
Alpha-Beta Pruning in artificial intelligence
https://youtu.be/xLO4PbwtLs4?si=9TfCJYYfmWnelZcE
MiniMax playing algorithm
https://youtu.be/R3P04TEacsk?si=ovhOjkplIDZk2n4M
OLTP vs OLAP in machine learning
https://youtu.be/AaLfbIfsUSY?si=AIYtTuftmMZPOfLh
Constraints Satisfaction problem in artificial intelligence
https://youtu.be/jITn6-VhgkM?si=YbJp3FhorFF5VJ3_
Hill Climbing Algorithm
https://youtu.be/qPAC0v_HvXU?si=yVvnqQAo3jg8D5RL
#goalbasedagent
#agoalBasedAgentinmachinelearning
#goalbasedartificialagent
#goalbasedagentsinartificialagent
Alpha-Beta Pruning in artificial intelligence
https://youtu.be/xLO4PbwtLs4?si=9TfCJYYfmWnelZcE
MiniMax playing algorithm
https://youtu.be/R3P04TEacsk?si=ovhOjkplIDZk2n4M
OLTP vs OLAP in machine learning
https://youtu.be/AaLfbIfsUSY?si=AIYtTuftmMZPOfLh
Constraints Satisfaction problem in artificial intelligence
https://youtu.be/jITn6-VhgkM?si=YbJp3FhorFF5VJ3_
Hill Climbing Algorithm
https://youtu.be/qPAC0v_HvXU?si=yVvnqQAo3jg8D5RL
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