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--- public:t-720-atai:atai-19:lecture_notes_architectures [2019/10/04 10:21] – [The SOAR Architecture] 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.   |
 |  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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 |  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.  |
-|  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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-[S] State-space search (example: GPS (Newell 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 (example: 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 (example: 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 (example: 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 (c.f. Muggleton 1994). A program is a statement with a procedural interpretation, which can be learned from given positive and negative examples, plus background knowledge.
-Besides the above fairly well-known AI approaches, we add here two less known ones that have recently become relevant in the context of self-programming:
-[E] Evidential reasoning (example: NARS (Wang 2006)). A program is a statement with a procedural interpretation, and it can be learned using multi-strategy (ampliative) uncertain reasoning. The details of this approach is described in the article by Wang in this issue.
-[A] Autocatalysis (example: 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.
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 ====The AERA System====
-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.
 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”).