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| public:t_720_atai:atai-18:lecture_notes_w2 [2019/08/21 14:02] – [Complexity of Agents] thorisson | public:t_720_atai:atai-18:lecture_notes_w2 [2024/04/29 13:33] (current) – external edit 127.0.0.1 |
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| =====T-720-ATAI-2018===== | =====T-720-ATAI-2018===== |
| ====Lecture Notes, W2: Agents & Control==== | ====Lecture Notes, W2: Agents & Control==== |
| | 2020(c)Kristinn R. Thórisson |
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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 controller has an input, <m>i_t</m>, selected from a task-environment, current state (<m>S</m>), at least one goal (<m>G</m>, implicit or explicit) whereby at least one is a Drive (<m>G_d</m>), and output <m>o_t</m> in the form of atomic actions (selected from a set of atomic possible outputs), and a set of processes (<m>P</m>). \\ The internals of a controller for the complex, adaptive control of a situated agent is referred to as //cognitive architecture//. \\ Any practical controller is //embodied//, in that it interacts with its environment through interfaces whereby its internal computations are turned into //physical actions:// <m>i</m> enters via //measuring devices// ("sensors") and <m>o</m> exits the controller via //effectors//. | | | An abstraction of a controller: A controller has an input, <m>i_t</m>, 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</m> in the form of atomic actions (selected from a set of atomic possible outputs), and a set of processes <m>P</m>. \\ The internals of a controller for the complex, adaptive control of a situated agent is referred to as //cognitive architecture//. \\ Any practical controller is //embodied//, in that it interacts with its environment through interfaces whereby its internal computations are turned into //physical actions:// <m>i</m> enters via //measuring devices// ("sensors") and <m>o</m> exits the controller via //effectors//. | |
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| | Why it is important | Goals are needed for concrete tasks, and tasks are a key part of why we would want AI in the first place. For any complex tasks there will be identifiable sub-goals -- talking about these in compressed manners (e.g. using natural language) is important for learning and for monitoring of task progress. | | | Why it is important | Goals are needed for concrete tasks, and tasks are a key part of why we would want AI in the first place. For any complex tasks there will be identifiable sub-goals -- talking about these in compressed manners (e.g. using natural language) is important for learning and for monitoring of task progress. | |
| | Historically speaking | Goals have been with the field of AI from the very beginning, but definitions vary. | | | Historically speaking | Goals have been with the field of AI from the very beginning, but definitions vary. | |
| | What to be aware of | We can assign goals to an AI without the AI having an explicit data structure that we can say matches the goal directly (see [[/public:t_720_atai:atai-18:lecture_notes_w2?&#braitenberg_vehicle_examples|Braitenberg Vehicles]] - below). These are called //**implicit goals**//. We may conjecture that if we want an AI to be able to talk about its goals they will have to be -- in some sense -- //**explicit**//, that is, having a discrete representation in the AI's "mind" that can be manipulated, inspected, compressed / decompressed, and related to other data structures for various purposes. | | | What to be aware of | We can assign goals to an AI without the AI having an explicit data structure that we can say matches the goal directly (see [[public:t_720_atai:atai-18:lecture_notes_w2#Example: Braitenberg Vehicles|Braitenberg Vehicles]] - below). These are called //**implicit goals**//. We may conjecture that if we want an AI to be able to talk about its goals they will have to be -- in some sense -- //**explicit**//, that is, having a discrete representation in the AI's "mind" that can be manipulated, inspected, compressed / decompressed, and related to other data structures for various purposes. | |
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| | Minimal agent | sensory data -> decision -> action | | | Minimal agent | single goal; inability to create sub-goals; sensory data -> decision -> action | |
| | Perception | Transducer that turns energy into information representation. | | | Perception | Transducer that turns energy into information representation. | |
| | Decision | Computation that uses perceptual data; chooses one alternative over (potentially) many for implementation. | | | Decision | Computation that uses perceptual data; chooses one alternative over (potentially) many for implementation. | |
| ====Complexity of Agents==== | ====Complexity of Agents==== |
| | Agent complexity | Determined by <m>I X P X O</m>, not just <m>P, i,</m> or <m>o</m>. | | | Agent complexity | Determined by <m>I X P X O</m>, not just <m>P, i,</m> or <m>o</m>. | |
| | Agent action complexity potential | Potential for P to control combinatorics of, or change, o, beyond initial i (at "birth"). | | | 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 input complexity potential | Potential for P to structure i in post-processing, and to extend i. | | | Agent input complexity potential | Potential for <m>P</m> to structure i in post-processing, and to extend <m>i</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 <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 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. | | | 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). | |
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| ====Reactive Agent Architecture==== | ====Reactive Agent Architecture==== |
| | Architecture | Largely fixed for the entire lifetime of the agent. | | | Architecture | Largely fixed for the entire lifetime of the agent. | |
| | super simple | Sensors connected directly to motors, e.g. Braitenberg Vehicles. | | | Why "Reactive"? | Named "reactive" because there is no prediction - the agent simply reacts to stimuli (sensory data) when/after it happens. | |
| | simple | Deterministic connections between components with small memory, e.g. chess engines, Roomba vacuum cleaner. | | | super simple | Sensors connected directly to motors, e.g. Braitenberg Vehicles. | |
| | 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. | | | simple | Deterministic connections between components with small memory, e.g. chess engines, Roomba vacuum cleaner. | |
| | 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. | | | 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 and Alexa. | |
| | | Super complex | Large number of modules (> 30) at various sizes, each with multiple relationships to others, at more than one LoC, e.g. some robots using the subsumption architecture. | |
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| ====Braitenberg Vehicle Examples==== | ====Example: Braitenberg Vehicles==== |
| | {{ :public:t-720-atai:love.png?150 }} | | | {{ :public:t-720-atai:love.png?150 }} | |
| | Braitenberg vehicle example control scheme: "love". Steers towards (and crashes into) that which its sensors sense. | | | Braitenberg vehicle example control scheme: "love". Steers towards (and crashes into) that which its sensors sense. | |
