public:t-720-atai:atai-16:lecture_notes_f-3_19.01.2016
Table of Contents
T-720-ATAI-2016
Lecture Notes, F-3 19.01.2016
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. |
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. |
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. |
Braitenberg Vehicle Examples
Subsumption Examples
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. |
Reinforcement Learning
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