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| public:t-720-atai:atai-19:lecture_notes_architectures [2019/10/07 16:22] – [Existing Systems Which Target Self-Programming] thorisson | public:t-720-atai:atai-19:lecture_notes_architectures [2024/04/29 13:33] (current) – external edit 127.0.0.1 |
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| | What it is | //Self-programming// here means, with respect to some virtual machine <m>M</m>, the production of one or more programs created by <m>M</m> itself, whose //principles// for creation were provided to <m>M</m> at design time, but whose details were //decided by <m>M</m>// at runtime based on its //experience//. | | | What it is | //Self-programming// here means, with respect to some virtual machine <m>M</m>, the production of one or more programs created by <m>M</m> itself, whose //principles// for creation were provided to <m>M</m> at design time, but whose details were //decided by// <m>M</m> //at runtime // based on its //experience//. | |
| | Self-Generated Program | Determined by some factors in the interaction between the system and its environment. | | | Self-Generated Program | Determined by some factors in the interaction between the system and its environment. | |
| | Historical note | Concept of self-programming is old (J. von Neumann one of the first to talk about self-replication in machines). However, few if any proposals for how to achieve this has been fielded. [[https://en.wikipedia.org/wiki/Von_Neumann_universal_constructor|Von Neumann's universal constructor on Wikipedia]] | | | Historical note | Concept of self-programming is old (J. von Neumann one of the first to talk about self-replication in machines). However, few if any proposals for how to achieve this has been fielded. [[https://en.wikipedia.org/wiki/Von_Neumann_universal_constructor|Von Neumann's universal constructor on Wikipedia]] | |
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| ====Levels of Self-Programming==== | |
| | Level 1 | Level one self-programming capability is the ability of a system to make programs that exclusively make use of its primitive actions from action set. | | |
| | Level 2 | Level two self-programming systems can do Level 1, and additionally generate new primitives. | | |
| | Level 3 | Level three self-programming adds the ability to change the principles by which Level one and Level two operate, in other words, Level three self-programming systems are capable of what we would here call meta-programming. This would involve changing or replacing some or all of the programs provided to the system at design time. Of course, the generations of primitives and the changes of principles are also controlled by some programs. | | |
| | Infinite regress? | Though the process of self-programming can be carried out in more than one level, eventually the regress will stop at a certain level. The more levels are involved, the more flexible the system will be, though at the same time it will be less stable and more complicated to be analyzed. | | |
| | Likely to be many ways? | For AGI the set of relevant self-programming approaches is likely to be a much smaller set than that typically discussed in computer science, and in all likelihood much smaller than often implied in AGI. | | |
| | Architecture | The possible solutions for effective and efficient self-programming are likely to be strongly linked to what we generally think of as the //architectural structure// of AI systems, since self-programming for AGI may fundamentally have to change, modify, or partly duplicate, some aspect of the architecture of the system, for the purpose of being better equipped to perform some task or set of tasks. | | |
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| ====Existing Systems Which Target Self-Programming==== | |
| ^ 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. | | |
| | [P] | Production system | SOAR (Laird 1987) | Each production rule specifies the condition for a sequence of actions that correspond to a program. Mechanisms that produce new production rules, such as chunking, can be considered self-programming. | | |
| | [R] | Reinforcement learning | AIXI (Hutter 2007) | When an action of an agent changes the state of the environment, and each state has a reward value associated, a program corresponds to a policy in reinforcement learning. When the state transition function is probabilistic, this becomes a Markov decision process. | | |
| | [G] | Genetic programming | Koza’s Invention Machine (Koza et al. 2000) | A program is formed from the system’s actions, initially randomly but subsequently via genetic operators over the best performers from prior solutions, possibly by using the output of some actions as input of some other actions. An evolution process provides a utility function that is used to select the best programs, and the process is repeated. | | |
| | [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. | | |
| | [A] | \\ Autocatalytic experience-based search over bi-directional models | 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 & Helgason 2012]] | | |
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| ====Design Assumptions in The Above Approaches==== | |
| | 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? | | |
| | 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? | | |
| | Can the system learn new knowledge about actions and goals? | a) Yes, and the learning process normally converges [G, I, R] \\ b) Yes, and the learning process may not converge [A, E, P] \\ c) No, all the knowledge are given or innate [S] \\ Relevant assumptions: \\ Are the goals constant or variable? \\ Are the actions constant or variable? | | |
| | What is the extent of resources demanded? | a) Unlimited time and/or space [I, R, S, P] \\ b) Limited time and space [A, E, G] \\ Relevant assumption: Are the resources used an attribute of the problem, or of the solution? | | |
| | When is the quality of a program evaluated? | a) After execution, according to its actual contribution [G] \\ b) Before execution, according to its definition or historical record [I, S, P] \\ c) Both of the above [A, E, R] \\ Relevant assumption: \\ Are adaptation and prediction necessary? | | |
| | source | [[http://alumni.media.mit.edu/~kris/ftp/JAGI-Special-Self-Progr-Editorial-ThorissonEtAl-09.pdf|Thórisson et al. 2012]] | | |
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| ====The AERA System==== | ====The AERA System==== |
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| The Auto-catalytic Endogenous Reflective Architecture – AERA – is an AGI-aspiring architectural blueprint that was produced as part of the HUMANOBS FP7 project. It encompasses several fundamentally new ideas, in the history of AI, including a new programming language specifically conceived to solve some major limitations of prior efforts in this respect, including self-inspection and self-representation, distributed representation of knowledge, and distributed reasoning. AERA systems are any-time, real-time, incremental/continuous learning, on-line learning systems. | The Auto-catalytic Endogenous Reflective Architecture – AERA – is an AGI-aspiring architectural blueprint that was produced as part of the HUMANOBS FP7 project. It encompasses several fundamentally new ideas in the history of AI, including a new programming language specifically conceived to solve some major limitations of prior efforts in this respect, including self-inspection and self-representation, distributed representation of knowledge, and distributed reasoning. AERA systems are any-time, real-time, incremental/continuous learning, on-line learning systems. |
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| AERA's knowledge is stored in models, which essentially encode transformations on input, to produce output. Models have a trigger side (left-hand side) and a result side (right-hand side). In a forward-chaining scenario, when a particular piece of data matches on the left hand of a model (it is only allowed to test the match if the data has high enough saliency and the program has sufficient activation) the model fires, producing the output specified by its left-hand side and injecting it into a global memory store. The semantics of the output is prediction, and the semantics of the input is either fact or prediction. Notice that a model in AERA is not a production rule; a model relating A to B does not mean “A entails B”, it means A predicts B, and it has an associated confidence value. Such models stem invariably (read: most of the time) from the system's experience, and in early stages of learning an AERA-based system's set of models may mostly consist fairly useless and bad models, all with relatively low confidence values (“not all models are created equal – some are in fact better than others”). | AERA's knowledge is stored in models, which essentially encode transformations on input, to produce output. Models have a trigger side (left-hand side) and a result side (right-hand side). In a forward-chaining scenario, when a particular piece of data matches on the left hand of a model (it is only allowed to test the match if the data has high enough saliency and the program has sufficient activation) the model fires, producing the output specified by its left-hand side and injecting it into a global memory store. The semantics of the output is prediction, and the semantics of the input is either fact or prediction. Notice that a model in AERA is not a production rule; a model relating A to B does not mean “A entails B”, it means A predicts B, and it has an associated confidence value. Such models stem invariably (read: most of the time) from the system's experience, and in early stages of learning an AERA-based system's set of models may mostly consist fairly useless and bad models, all with relatively low confidence values (“not all models are created equal – some are in fact better than others”). |
