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| public:t-720-atai:atai-24:agents_and_control [2024/01/30 14:12] – [Reactive ("Feedback") Agent Architecture] thorisson | public:t-720-atai:atai-24:agents_and_control [2024/04/29 13:33] (current) – external edit 127.0.0.1 |
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| | {{ :public:t-720-atai:abstract-agent.png?250 }} | | | {{ :public:t-720-atai:abstract-agent.png?250 }} | |
| | An abstraction of a controller: A set of //processes// <m>P</m> that can receive an input, <m>i_t~in~I</m>, produced by and selected from a task-environment, current state <m>S</m>, at least one goal <m>G</m> (implicit or explicit - see table below) and output <m>o_t~in~O</m> in the form of atomic actions (selected from a set of atomic possible outputs <m>O</m>), that (in the limit) achieve goal(s) G. | | | An abstraction of a controller: A set of //processes// **P** that can receive an input, **i_t~in~I**, produced by and selected from a task-environment, current state **S**, at least one goal **G** (implicit or explicit - see table below) and output **o_t~in~O** in the form of atomic actions (selected from a set of atomic possible outputs **O**), that (in the limit) achieve goal(s) G. | |
| | The internals of a controller for the complex, adaptive control of a situated agent is referred to as //cognitive architecture//. | | | The internals of a controller for the complex, adaptive control of a situated agent is referred to as //cognitive architecture//. | |
| | Any practical, operational controller is //embodied//, in that it interacts with its environment through interfaces whereby its internal computations are turned into //physical actions// of some form or other. Input <m>i~in~I</m> enters via //measuring devices// or //**sensors**//, and <m>o_t~in~O</m> exits the controller via //**effectors**//. | | | Any practical, operational controller is //embodied//, in that it interacts with its environment through interfaces whereby its internal computations are turned into //physical actions// of some form or other. Input **i~in~I** enters via //measuring devices// or //**sensors**//, and **o_t~in~O** exits the controller via //**effectors**//. | |
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| | How Can That Be Done? | How can an AI architecture learn not only multiple co-dependent control pipelines, but also how they may be relevant in some situations and not others, as well as how their setpoints may change depending on context? | | | How Can That Be Done? | How can an AI architecture learn not only multiple co-dependent control pipelines, but also how they may be relevant in some situations and not others, as well as how their setpoints may change depending on context? | |
| | Models | Conant & Ashby showed in [[http://cleamc11.vub.ac.be/books/Conant_Ashby.pdf|1970]] that any good controller of a system //must harbor a **model**// of that system. \\ We will address //models// in the SYMBOLS, MODELS, CAUSATION sprint (see Canvas modules). | | | Models | Conant & Ashby showed in [[http://cleamc11.vub.ac.be/books/Conant_Ashby.pdf|1970]] that any good controller of a system //must harbor a **model**// of that system. \\ We will address //models// in the SYMBOLS, MODELS, CAUSATION sprint (see Canvas modules). | |
| | Agent complexity | Determined by <m>I chi P chi O</m>, not just <m>P, i,</m> or <m>o</m>. | | | Agent complexity | Determined by **I chi P chi O**, not just **P, i,** or **o**. | |
| | Agent action complexity potential | \\ Potential for <m>P</m> to control combinatorics of, or change, <m>o</m>, beyond initial <m>i</m> (at "birth"). | | | 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 <m>P</m> to structure <m>i</m> in post-processing, and to extend <m>i</m>. | | | Agent input complexity potential | \\ Potential for **P** to structure <m>i</m> in post-processing, and to extend **i**. | |
| | Agent <m>P</m> complexity potential | \\ Potential for <m>P</m> to acquire and effectively and efficiently store and access past <m>i</m> (learning); potential for <m>P</m> to change <m>P</m>. | | | 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 <m>P</m> to coherently coordinate all of the above to improve its own ability to use its resources, acquire more resources, in light of drives (top-level goals). | | | Agent intelligence potential | Potential for **P** to coherently coordinate all of the above to improve its own ability to use its resources, acquire more resources, in light of drives (top-level goals). | |
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| ====Reactive ("Feedback") Agent Architecture==== | ====Reactive ("Feedback") Agent Architecture==== |
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| | Feedback | Reacts to measurements. Change happens in light of a received measurement, in which case a control signal <m>v</m> can be produced //after// perturbations of <m>v</m> happens, so that the output of the plant <m>o</m> can catch up with the change. | | | Feedback | Reacts to measurements. Change happens in light of a received measurement, in which case a control signal **v** can be produced //after// perturbations of <m>v</m> happens, so that the output of the plant **o** can catch up with the change. | |
| | What it requires | This requires data from sensors. | | | What it requires | This requires data from sensors. | |
| | Signal Behavior | When reacting to a time-varying signal <m>v</m> the frequency of change, the possible patterns of change, and the magnitude of change of <m>v</m>; latency and jitter can produce unstoppable fluctuations. | | | Signal Behavior | When reacting to a time-varying signal **v** the frequency of change, the possible patterns of change, and the magnitude of change of **v**; latency and jitter can produce unstoppable fluctuations. | |
| | Architecture | Largely fixed for the entire lifetime of the agent. \\ Agent may learn but acts only in reaction to experience (no prediction). | | | Architecture | Largely fixed for the entire lifetime of the agent. \\ Agent may learn but acts only in reaction to experience (no prediction). | |
| | Learning reactive control | \\ Associating reactions to situations. | | | Learning reactive control | \\ Associating reactions to situations. | |
