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| public:t_720_atai:atai-18:lecture_notes_w4 [2018/09/05 09:08] – [Systems With Self-Programming Potential] thorisson | public:t_720_atai:atai-18:lecture_notes_w4 [2024/04/29 13:33] (current) – external edit 127.0.0.1 |
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| ==== Autonomy & Closure ==== | ==== Autonomy & Closure ==== |
| | Autonomy | The ability to do tasks without interference / help from others. | | | Autonomy | The ability to do tasks without interference / help from others in a particular task-environment in a particular world. | |
| | Cognitive Autonomy | Refers to the mental (control-) independence of agents - the more independent they are (of their designers, of outside aid, etc.) the more autonomous they are. Systems without it could hardly be considered to have general intelligence. | | | Cognitive Autonomy | Refers to the mental (control-) independence of agents - the more independent they are (of their designers, of outside aid, etc.) the more autonomous they are. Systems without it could hardly be considered to have general intelligence. | |
| | Structural Autonomy | Refers to the process through which cognitive autonomy is achieved: Motivations, goals and behaviors as dynamically and continuously (re)constructed by the machine as a result of changes in its internal structure. | | | Structural Autonomy | Refers to the process through which cognitive autonomy is achieved: Motivations, goals and behaviors as dynamically and continuously (re)constructed by the machine as a result of changes in its internal structure. | |
| | Operational closure | The system's own operations is all that is required to maintain (and improve) the system itself. | | | Operational closure | The system's own operations is all that is required to maintain (and improve) the system itself. | |
| | Semantic closure | The system's own operations and experience produces/defines the meaning of its constituents. //Meaning// can thus be seen as being defined/given by the operation of the system as a whole: the actions it has taking, is taking, could be taking, and has thought about (simulated) taking, both cognitive actions and external actions in its physical domain. For instance, the meaning of punching your best friend are the implications - actual and potential - that this action has/may have, and its impact on your own cognition. | | | Semantic closure | The system's own operations and experience produces/defines the meaning of its constituents. //Meaning// can thus be seen as being defined/given by the operation of the system as a whole: the actions it has taken, is taking, could be taking, and has thought about (simulated) taking, both cognitive actions and external actions in its physical domain. For instance, the meaning of punching your best friend are the implications - actual and potential - that this action has/may have, and its impact on your own cognition. | |
| | Self-Programming in Autonomy | The global process that animates computational structurally autonomous systems, i.e. the implementation of both the operational and semantic closures. | | | Self-Programming in Autonomy | The global process that animates computational structurally autonomous systems, i.e. the implementation of both the operational and semantic closures. | |
| | System evolution | A controlled and planned reflective process; a global and never-terminating process of architectural synthesis. | | | System evolution | A controlled and planned reflective process; a global and never-terminating process of architectural synthesis. | |
| ==== Autonomy ==== | ==== Autonomy ==== |
| | {{public:t-720-atai:autonomy-dimensions1.png}} | | | {{public:t-720-atai:autonomy-dimensions1.png}} | |
| | “Autonomy comparison framework focusing on mental capabilities. Embodiment is not part of the present framework, but is included here for contextual completeness.” //From Thorisson & Helgason 2012// [[http://xenia.media.mit.edu/~kris/ftp/AutonomyCogArchReview-ThorissonHelgason-JAGI-2012.pdf|source]] | | | “Autonomy comparison framework focusing on mental capabilities. Embodiment is not part of the present framework, but is included here for contextual completeness.” //From Thorisson & Helgason 2012// [[http://alumni.media.mit.edu/~kris/ftp/AutonomyCogArchReview-ThorissonHelgason-JAGI-2012.pdf|source]] | |
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| | Why? | Building a machine that can write (sensible, meaningful!) programs means that machine is smart enough to understand the code it produces. If the purpose of its programming is to //become//smart, and the programming language we give to it //assumes it's smart already//, we have defeated the purpose of creating the self-programming machine in the first place. | | | Why? | Building a machine that can write (sensible, meaningful!) programs means that machine is smart enough to understand the code it produces. If the purpose of its programming is to //become//smart, and the programming language we give to it //assumes it's smart already//, we have defeated the purpose of creating the self-programming machine in the first place. | |
| | What can we do? | We must create a programming language with //simple enough// semantics so that a simple machine (perhaps with some clever emergent properties) can use it to bootstrap itself in learning to write programs. | | | What can we do? | We must create a programming language with //simple enough// semantics so that a simple machine (perhaps with some clever emergent properties) can use it to bootstrap itself in learning to write programs. | |
| | Does such a language exist? | Yes. It's called [[http://xenia.media.mit.edu/~kris/ftp/nivel_thorisson_replicode_AGI13.pdf|Replicode]]. | | | Does such a language exist? | Yes. It's called [[http://alumni.media.mit.edu/~kris/ftp/nivel_thorisson_replicode_AGI13.pdf|Replicode]]. | |
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| ====Existing Systems With Self-Programming Aims & Potential==== | ====Existing Systems Which Target Self-Programming==== |
| ^ Label ^ What ^ Example ^Description^ | ^ Label ^ What ^ Example ^Description^ |
| | [S] | State-space search | GPS (Newell et al. 1963) | The atomic actions are state-changing operators, and a program is represented as a path from the initial state to a final state. Variants of this approach include program search (examples: Gödel Machine (Schmidhuber 2006)): Given the action set A, in principle all programs formed by it can be exhaustively listed and evaluated to find an optimal one according to certain criteria. | | | [S] | State-space search | GPS (Newell et al. 1963) | The atomic actions are state-changing operators, and a program is represented as a path from the initial state to a final state. Variants of this approach include program search (examples: Gödel Machine (Schmidhuber 2006)): Given the action set A, in principle all programs formed by it can be exhaustively listed and evaluated to find an optimal one according to certain criteria. | |
| | [I] | Inductive logic programming | Muggleton 1994 | A program is a statement with a procedural interpretation, which can be learned from given positive and negative examples, plus background knowledge. | | | [I] | Inductive logic programming | Muggleton 1994 | A program is a statement with a procedural interpretation, which can be learned from given positive and negative examples, plus background knowledge. | |
| | [E] | Evidential reasoning | NARS (Wang 2006) | A program is a statement with a procedural interpretation, and it can be learned using multi-strategy (ampliative) uncertain reasoning. | | | [E] | Evidential reasoning | NARS (Wang 2006) | A program is a statement with a procedural interpretation, and it can be learned using multi-strategy (ampliative) uncertain reasoning. | |
| | [A] | Autocatalysis | AERA (Nivel et al. 2014) \\ & \\ Ikon Flux (Nivel 2007) | In this context the architecture is in large part comprised of a large collection of models, acting as hierarchically organized controllers, executed through a contextually-informed, continuous auto-catalytic process. New models are produced automatically, based on experience, their quality evaluated in light of this experience, and improvements produced as a result. Self-programming occurs at two levels: The lower one is concerned with performance in a set of domains, making models of how best to achieve goals in the external world at any point in time, the higher level is concerned with the operation of the lower one, implementing integrated cognitive control and meta-learning capabilities. Semantically closed auto-catalytic processes maintain the system’s growth after they are deployed. | | | [A] | Autocatalytic model-driven bi-directional search | AERA (Nivel et al. 2014) \\ & \\ Ikon Flux (Nivel 2007) | In this context the architecture is in large part comprised of a large collection of models, acting as hierarchically organized controllers, executed through a contextually-informed, continuous auto-catalytic process. New models are produced automatically, based on experience, their quality evaluated in light of this experience, and improvements produced as a result. Self-programming occurs at two levels: The lower one is concerned with performance in a set of domains, making models of how best to achieve goals in the external world at any point in time, the higher level is concerned with the operation of the lower one, implementing integrated cognitive control and meta-learning capabilities. Semantically closed auto-catalytic processes maintain the system’s growth after they are deployed. | |
| | source | [[http://alumni.media.mit.edu/~kris/ftp/JAGI-Special-Self-Progr-Editorial-ThorissonEtAl-09.pdf|Thórisson et al. 2012]] | | | source | [[http://alumni.media.mit.edu/~kris/ftp/JAGI-Special-Self-Progr-Editorial-ThorissonEtAl-09.pdf|Thórisson & Helgason 2012]] | |
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| ====Design Assumptions in The Above Approaches==== | ====Design Assumptions in The Above Approaches==== |
| | How does the system represent each basic action? | a) As an operator that transforms a state to another state, either deterministically or probably, and goal as state to be reached [R, S] \\ b) As a function that maps some input arguments to some output arguments [G] \\ c) As a realizable statement with preconditions and consequences [A, E, I, P] \\ Relevant assumptions: \\ Is the knowledge about an action complete and certain? \\ Is the action set discrete and finite? | | | How does the system represent a basic action? | a) As an operator that transforms a state to another state, either deterministically or probably, and goal as state to be reached [R, S] \\ b) As a function that maps some input arguments to some output arguments [G] \\ c) As a realizable statement with preconditions and consequences [A, E, I, P] \\ Relevant assumptions: \\ Is the knowledge about an action complete and certain? \\ Is the action set discrete and finite? | |
| | Can a program be used as an action in other programs? | a) Yes, programs can be built recursively [A, E, G, I] \\ b) No, a program can only contain basic actions [R, S, P] \\ Relevant assumptions: \\ Do the programs and actions form a hierarchy? \\ Can these recursions have closed loops? | | | Can a program be used as an "action" in other programs? | a) Yes, programs can be built recursively [A, E, G, I] \\ b) No, a program can only contain basic actions [R, S, P] \\ Relevant assumptions: \\ Do the programs and actions form a hierarchy? \\ Can these recursions have closed loops? | |
| | How does the system represent goals? | a) As states to be reached [S] \\ b) As values to be optimized [G, R] \\ c) As statements to be realized [E, P, A] \\ d) As functions to be approximated [I] \\ Relevant assumptions: \\ Is the knowledge about goals complete? \\ Is the knowledge about goals certain? \\ Can all the goals be reached with a concrete action set? | | | How does the system represent goals? | a) As states to be reached [S] \\ b) As values to be optimized [G, R] \\ c) As statements to be realized [E, P, A] \\ d) As functions to be approximated [I] \\ Relevant assumptions: \\ Is the knowledge about goals complete? \\ Is the knowledge about goals certain? \\ Can all the goals be reached with a concrete action set? | |
| | Are there derived goals? | a) Yes, and they are logically dependent to the original goals [I, S, P] \\ b) Yes, and they may become logically independent to the original goals [A, E] \\ c) No, all goals are given or innate [G, R] \\ Relevant assumptions: \\ Are the goals constant or variable? \\ Are the goals externally imposed or internally generated? | | | Are there derived goals? | a) Yes, and they are logically dependent to the original goals [I, S, P] \\ b) Yes, and they may become logically independent to the original goals [A, E] \\ c) No, all goals are given or innate [G, R] \\ Relevant assumptions: \\ Are the goals constant or variable? \\ Are the goals externally imposed or internally generated? | |
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| | ==== Cognitive Growth ==== |
| | | What it is | Changes in the cognitive controller (the core "thinking" part) over and beyond basic learning: After a growth burst of this kind the controller can learn differently/better/new things, especially new //categories// of things. | |
| | | Human example | [[https://m.youtube.com/watch?v=TRF27F2bn-A|Piaget's Stages of Development (youtube video)]] | |
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| | 2018(c)K.R.Thórisson \\ |
| //EOF// | //EOF// |
