[[http://cadia.ru.is/wiki/public:t-720-atai:atai-16:main|T-720-ATAI-2016 Main]]


=====T-720-ATAI-2016=====
====Lecture Notes, F-3	19.01.2016====
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====Agents====
|  Minimal agent  | sensory data -> decision -> action   |
|  Perception  | Transducer that turns energy into information representation.  |
|  Decision  | Computation that uses perceptual data; chooses one alternative over (potentially) many for implementation.  |
|  Action  | Potential of the Agent to influence its task-environment, e.g. to move its body, grasp an object, utter some words, etc. Decisions turned into Actions produces Behavior.  |
|  Learning agent  | Uses memory to enhance decisions.  |


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|  {{ :public:t-720-atai:abstract-agent.png?250 }}  |
|  An abstraction of an agent: An agent has an input (i, selected from a task-environment), current state (S), goal (G, implicit or explicit), and output (o) in the form of atomic actions (selected from possible output), and a set of processes (P).  |

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====Complexity of Agents====
|  Agent complexity  | Determined, at a minimum, by iXPXo, not just P, i, or o. \\ Taking time into account, as we should, an adaptive agent is of course more complex than the minimum (in the case of human-level intelligence, //much// more complex).  |
|  Agent action complexity potential  | Potential for P to control combinatorics of, or change, o, beyond initial i (at "birth").   |
|  Agent input complexity potential  | Potential for P to structure i in post-processing, and to extend i.  |
|  Agent P complexity potential  | Potential for P to acquire and effectively and efficiently store and access past i (learning); potential for P to change P.  |
|  Agent intelligence potential  | Potential for P to coherently coordinate the above to improve the agent's ability to use its resources, or acquire more resources, to achieve top-level goals.  |



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====Reactive Agent Architecture====
| Architecture  | Largely fixed for the entire lifetime of the agent.  |
| super simple | Sensors connected directly to motors, e.g. Braitenberg Vehicles. |
| simple | Deterministic connections between components with small memory, e.g. chess engines, Roomba vacuum cleaner.  |
| Complex | Grossly modular architecture (< 30 modules) with multiple relationships at more than one level of control detail (LoC), e.g. speech-controlled dialogue systems like Siri.   |
| Super complex | Large number of modules (> 30) at various sizes, each with multiple relationships to others, at more than one LoC, e.g. subsumption architecture.  |

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====Braitenberg Vehicle Examples====
| {{ :public:t-720-atai:love.png?150 }} |
|  Braitenberg vehicle example control scheme: "love". Steers towards (and crashes into) that which its sensors sense.  |
| {{ :public:t-720-atai:hate.png?150 }} |
|  Braitenberg vehicle example control scheme: "hate". Avoids that which it senses.  |
| {{ :public:t-720-atai:curous.png?150 }} |
|  Braitenberg vehicle example control scheme: "curious". The thinner wires are weighted-down signals, changing the behavior of "love" by avoiding crashing into things.  |

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====Subsumption Examples====
| {{ :public:t-720-atai:subsumption-arch-module-1.gif?450 }} |
|  Subsumption control architecture building block.  |
| {{ :public:t-720-atai:subsumption-arch-2.jpg?450 }} |
|  Example subsumption architecture for robot.  |
| {{ :public:t-720-atai:subsump-level0.png?450 }} |
|  Subsumption architecture example, level 0.  |
| {{ :public:t-720-atai:subsump-level1.png?450 }} |
|  Subsumption architecture example, level 1.  |
|  {{ :public:t-720-atai:subsump-level2.png?450 }}  |
|  Subsumption architecture example, level 2.  |



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====Model-Acquiring Agents====
|  Model  | A model of something is an information structure that behaves in some ways like the thing being modeled.   |
|  A good model of X | ...allows What-If questions to be asked about X, with answers correctly predicting what happens to X.  |
|  Model acquisition  | The ability to create models of (observed) phenomena.   |



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====Reinforcement Learning====
Download Jordi's slides {{:public:t-720-atai:atai-16:20160119_reinforcement_learning.pdf|here}}, and see reading and study materials [[http://cadia.ru.is/wiki/public:t-720-atai:atai-16:readings#reinforcement_learning|here]].


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2016(c)K. R. Thórisson 
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