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LEARNING IN AGENTS

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The document discusses machine learning and learning agents in three main points: 1. It defines machine learning and discusses different types of machine learning tasks like supervised, unsupervised, and reinforcement learning. 2. It explains the key differences between traditional machine learning approaches and learning agents, noting that learning is one of many goals for agents and must be integrated with other agent functions. 3. It discusses different challenges of integrating machine learning into intelligent agents, such as balancing learning with recall of existing knowledge and addressing time constraints on learning from the environment.

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Learning in Agents Material collected, assembled and extended by S. Costantini, Computer Sc. Dept. Univ. of L’Aquila Many thanks to all colleagues that share teaching material on the web.
Why Learning Agents? ,[object Object],[object Object],[object Object]
What is Machine Learning? ,[object Object],[object Object]
ML (machine learning): Another View ,[object Object],[object Object],[object Object],[object Object]
When an agent learns: ,[object Object],[object Object],[object Object]
Machine Learning Biases ,[object Object],[object Object],[object Object],[object Object]
Concept Language and Black- vs. White-Box Learning ,[object Object],[object Object]
Machine Learning vs . Learning Agents ,[object Object],Classic Machine Learning Active Learning Learning as one of many goals: Learning Agent(s) Closed Loop Machine Learning
Integrating Machine Learning into the Agent Architecture ,[object Object],[object Object],[object Object],[object Object]
Time Constraints on Learning ,[object Object],[object Object],[object Object],[object Object],[object Object]
Learning and Recall ,[object Object],[object Object],[object Object]
Learning and Recall (2) Update sensory information Recall current model of world to choose and carry out an action Learn new model of the world ,[object Object],[object Object]
Learning and Recall (3) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Types of Learning Task ,[object Object],[object Object],[object Object]
Learning to Coordinate ,[object Object],[object Object],[object Object],[object Object],[object Object]
Soccer Formation
Soccer Formation Control ,[object Object],[object Object],[object Object],[object Object]
Learning as Knowledge Extraction ,[object Object],[object Object]
Data Mining Taxonomy ,[object Object],[object Object],[object Object],[object Object]
Definition of Data Mining ,[object Object],[object Object]
Overview ,[object Object],[object Object],[object Object],[object Object],[object Object]
Classification & Regression ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Clustering & Association ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Deviation Detection & Pattern Discovery ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Overview ,[object Object],[object Object],[object Object],[object Object],[object Object]
Data Mining Models & Algorithms ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Neural Networks ,[object Object],[object Object],[object Object],1 2 3 4 5 6 Inputs Output Hidden Layer
Neural Networks (cont.) ,[object Object],[object Object],[object Object],[object Object],[object Object]
Decision Trees (cont.) ,[object Object],[object Object]
Decision Trees ,[object Object],[object Object],[object Object],[object Object],[object Object],No Yes Yes No Yes No
Rule Induction ,[object Object],[object Object],[object Object],[object Object]
K-nearest neighbor ,[object Object],[object Object],X X x X Y x X N X X Y
Artificial Neural Networks
Introduction ,[object Object],[object Object],[object Object]
Introduction ,[object Object],[object Object],[object Object]
Biological Neural Systems ,[object Object],Schematic drawing of two biological neurons connected by synapses A typical neuron collects signals from other neurons through a host of fine structures called dendrites . T he neuron sends out spikes of electrical activity through a long, thin strand known as an axon , which splits into thousands of branches. A t the end of the branch, a structure called a synapse converts the activity from the axon into electrical effects that inhibit or excite activity in the connected neurons. W hen a neuron receives excitatory input that is sufficiently large compared with its inhibitory input, it sends a spike of electrical activity down its axon. Learning occurs by changing the effectiveness of the synapses so that the influence of one neuron on the other changes
What is a Neural Net? ,[object Object],[object Object]
Components Needed: ,[object Object],[object Object],[object Object],[object Object],[object Object]
How does a neural network learn? ,[object Object],[object Object],[object Object],[object Object]
Artificial Neural Networks ,[object Object],[object Object],An artificial neuron where w i is the strength of the synapse connected to the neuron, x i is an input feature to the neuron
Artificial Neural Networks ,[object Object],[object Object],[object Object],[object Object],[object Object]
Artificial Neural Networks ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Neural Network Architectures ,[object Object],[object Object],[object Object]
Neural Network Applications ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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LEARNING IN AGENTS

  • 1. Learning in Agents Material collected, assembled and extended by S. Costantini, Computer Sc. Dept. Univ. of L’Aquila Many thanks to all colleagues that share teaching material on the web.
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