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| public:t-720-atai:atai-24:agents_and_control [2024/01/30 14:13] – [Reactive ("Feedback") Agent Architecture] thorisson | public:t-720-atai:atai-24:agents_and_control [2025/04/27 11:19] (current) – [Key Concepts in Control] thorisson |
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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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| | Transducer | A device that changes one type of energy to another, typically amplifying and/or dampening the energy in the process. | | | Transducer | A device that changes one type of energy to another, typically amplifying and/or dampening the energy in the process. | |
| | Actuator | A physical (or virtual) transduction mechanism that implements an action that a controller has committed to. | | | Actuator | A physical (or virtual) transduction mechanism that implements an action that a controller has committed to. | |
| | Control Connection | Predefined causal connection between a measured variable <m>v</m> and a controllable variable <m>v_c</m> where <m>v = f(v_c)</m>. | | | Control Connection | Predefined causal connection between a measured variable <m>v</m> and a controllable variable vc where v = f(vc) | |
| | Mechanical Controller | Fuses control mechanism with measurement mechanism via mechanical coupling. Adaptation would require mechanical structure to change. Makes adaptation very difficult to implement. | | | Mechanical Controller | Fuses control mechanism with measurement mechanism via mechanical coupling. Adaptation would require mechanical structure to change. Makes adaptation very difficult to implement. | |
| | Digital Controller | Separates the stages of measurement, analysis, and control. Makes adaptive control in machines feasible. | | | Digital Controller | Separates the stages of measurement, analysis, and control. Makes adaptive control in machines feasible. | |
| | \\ Feedback | For a variable <m>v</m>, information of its value at time <m>t_1</m> is transmitted back to the controller through a feedback mechanism as <m>v{prime}</m>, where \\ <m>v{prime}(t) > v(t)</m> \\ that is, there is a //latency// in the transmission, which is a function of the speed of transmission (encoding (measurement) time + transmission time + decoding (read-back) time). | | | \\ Feedback | For a variable v, information of its value at time t1is transmitted back to the controller through a feedback mechanism as v', where \\ v'(t) > v(t) \\ that is, there is a //latency// in the transmission, which is a function of the speed of transmission (encoding (measurement) time + transmission time + decoding (read-back) time). | |
| | Latency | A measure for the size of the difference between <m>v</m> and <m>v{prime}</m>. | | | Latency | A measure for the size of the difference between <m>v</m> and <m>v{prime}</m>. | |
| | Jitter | The change in Latency over time. Second-order latency. | | | Jitter | The change in Latency over time. Second-order latency. | |
| | 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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