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| [[/public:t-720-atai:atai-24:main|T-720-ATAI-2024 Main]] \\ | [[/public:t-713-mers:mers-24:main|DCS-T-713-MERS-2024 Main]] \\ |
| [[/public:t-720-atai:atai-24:Lecture_Notes|Links to Lecture Notes]] | [[/public:t-713-mers:mers-24:lecture_notes|Lecture Notes]] |
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| ===== AI Architectures ===== | ====== Empirical Reasoning IV: AI Architectures ====== |
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| ====System Architecture==== | =====System Architecture===== |
| | What it is | In CS: the organization of the software that implements a system. \\ In AI: The total system that has direct and independent control of the behavior of an Agent via its sensors and effectors. | | | What it is | In CS: the organization of the software that implements a system. \\ In AI: The total system that has direct and independent control of the behavior of an Agent via its sensors and effectors. | |
| | Why it's important | The system architecture determines what kind of information processing can be done, and what the system as a whole is capable of in a particular Task-Environemnt. | | | Why it's important | The system architecture determines what kind of information processing can be done, and what the system as a whole is capable of in a particular Task-Environemnt. | |
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| ====Inferred GMI Architectural Features ==== | ===== Desired Empirical Reasoning Architectural Features ===== |
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| | \\ Predictable Robustness in Novel Circumstances | The system must have a robustness in light of all kinds of task-environment and embodiment perturbations, otherwise no reliable plans can be made, and thus no reliable execution of tasks can ever be reached, no matter how powerful the learning capacity. This robustness must be predictable a-priori at some level of abstraction -- for a wide range of novel circumstances it cannot be a complete surprise that the system "holds up". (If this were the case then the system itself would not be able to predict its chances of success in face of novel circumstances, thus eliminating an important part of the "G" from its "GMI" label.) || | | \\ Predictable Robustness in Novel Circumstances | The system must have a robustness in light of all kinds of task-environment and embodiment perturbations, otherwise no reliable plans can be made, and thus no reliable execution of tasks can ever be reached, no matter how powerful the learning capacity. This robustness must be predictable a-priori at some level of abstraction -- for a wide range of novel circumstances it cannot be a complete surprise that the system "holds up". (If this were the case then the system itself would not be able to predict its chances of success in face of novel circumstances, thus eliminating an important part of the "G" from its "GMI" label.) || |
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| ====SOAR==== | =====SOAR===== |
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| | What it is | One of the oldest cognitive architectures in history. | | | What it is | One of the oldest cognitive architectures in history. | |
| | Why is it important | One of the oldest AGI-aspiring systems in history. | | | Why is it important | One of the oldest AGI-aspiring systems in history. \\ (It could also be argued that most systems built in the first 30 years of AI research (1956 to 1986) were all "aspiring AGI systems".) | |
| | How does it work | Reasoning engine does pattern-matching with hand-coded 'production' rules and 'operators' to solve problems, with an ability to "chunk" - create 'shortcuts' for long transitive reasoning chains. Upon 'impasse' (break in the flow of reasoning/problem solving) a reasoning process tries to resolve it via successive application of relevant rules. | | | How does it work | Reasoning engine does pattern-matching with hand-coded 'production' rules and 'operators' to solve problems, with an ability to "chunk" - create 'shortcuts' for long transitive reasoning chains. Upon 'impasse' (break in the flow of reasoning/problem solving) a reasoning process tries to resolve it via successive application of relevant rules. | |
| | Recent Additions | Reinforcement learning for steering reasoning. Sub-symbolic processing for low-level perception. | | | Recent Additions | Reinforcement learning for steering reasoning. Sub-symbolic processing for low-level perception. | |
| | Missing in Action | Attention (resource control, self-control), symbolic learning (other than chunking). | | | Missing in Action | Attention (resource control, self-control), symbolic learning (other than chunking). | |
| | \\ \\ General Description | SOAR has been used by many researchers worldwide during its 20 year life span. During this time it has also been revised and extended in a number of ways. The architecture consists of heterogenous components that interact during each decision cycle. These are working memory and three types of long-term memory: **semantic**, **procedural**, and **episodic**. Working memory is where information related to the present is stored with its contents being supplied by sensors or copied from other memory structures based on relevancy to the present situation. Working memory also contains an activation mechanism, used in conjunction with episodic memory, that indicates the relevancy and usefulness of working memory elements. Production rules are matched and fired on the contents of working memory during the decision cycle, implementing both an associative memory mechanism (as rules can bring data from long-term memory into working memory) and action selection (as rules propose, evaluate and apply operators). Operators are procedural data stored in procedural memory. The application of an operator is carried out by a production rule and either causes changes in the working memory or triggers an external action. In cases where operator selection fails due to insufficient knowledge, an impasse event occurs and a process to resolve the impasse is started. This process involves reasoning and inference upon existing knowledge using the same decision cycle in a recursive fashion, the results of this process are converted to production rules by a process termed chunking. Reinforcement learning is used for production rules relating to operator selection to maximize future rewards in similar situations. One of the most recent additions to the SOAR architecture is sub-symbolic processing used for visual capabilities, where the bridge between sub-symbolic to symbolic processing consists of feature detection. As the working memory can contain execution traces, introspective abilities are possible. \\ \\ The SOAR architecture provides one of the largest collection of simultaneous running cognitive processes of any cognitive architecture so far. However, there is no explicit mechanism for control of attention and the architecture is not designed for real-time operation. The latter may be especially problematic as execution is in strict step-lock form and in particular, the duration (amount of computation) in each decision cycle can vary greatly due to impasse events that are raised occasionally. One might argue that the development of SOAR has been somewhat characterized by "adding boxes" (components) to the architecture when it might be better to follow a more unified approach putting integration at the forefront. \\ \\ There are a few cognitive architectures that somewhat resemble SOAR and can be placed categorically on the same track. These include ICARUS, which has a strong emphasis on embodiment and has shown promise in terms of generality in a number of toy problems such as in-city driving, and LIDA which was developed for the US Navy to automatically organize and negotiate assignments with sailors but does not have embodiment as a design goal. As in SOAR, both of these implement different types of memory in specialized components and have a step-locked decision cycle. \\ \\ REF: Helgason, H.P. (2013). [[http://www.ru.is/media/td/Helgi_Pall_Helgason_PhD_CS_HR.pdf|General Attention Mechanisms for Artificial Intelligence Systems]]. Ph.D. Thesis, School of Computer Science, Reykjavik U. | | | \\ \\ General Description of SOAR | SOAR has been used by many researchers worldwide during its 20 year life span. During this time it has also been revised and extended in a number of ways. The architecture consists of heterogenous components that interact during each decision cycle. These are working memory and three types of long-term memory: **semantic**, **procedural**, and **episodic**. Working memory is where information related to the present is stored with its contents being supplied by sensors or copied from other memory structures based on relevancy to the present situation. Working memory also contains an activation mechanism, used in conjunction with episodic memory, that indicates the relevancy and usefulness of working memory elements. Production rules are matched and fired on the contents of working memory during the decision cycle, implementing both an associative memory mechanism (as rules can bring data from long-term memory into working memory) and action selection (as rules propose, evaluate and apply operators). Operators are procedural data stored in procedural memory. The application of an operator is carried out by a production rule and either causes changes in the working memory or triggers an external action. In cases where operator selection fails due to insufficient knowledge, an impasse event occurs and a process to resolve the impasse is started. This process involves reasoning and inference upon existing knowledge using the same decision cycle in a recursive fashion, the results of this process are converted to production rules by a process termed chunking. Reinforcement learning is used for production rules relating to operator selection to maximize future rewards in similar situations. One of the most recent additions to the SOAR architecture is sub-symbolic processing used for visual capabilities, where the bridge between sub-symbolic to symbolic processing consists of feature detection. As the working memory can contain execution traces, introspective abilities are possible. \\ \\ The SOAR architecture provides one of the largest collection of simultaneous running cognitive processes of any cognitive architecture so far. However, there is no explicit mechanism for control of attention and the architecture is not designed for real-time operation. The latter may be especially problematic as execution is in strict step-lock form and in particular, the duration (amount of computation) in each decision cycle can vary greatly due to impasse events that are raised occasionally. One might argue that the development of SOAR has been somewhat characterized by "adding boxes" (components) to the architecture when it might be better to follow a more unified approach putting integration at the forefront. \\ \\ There are a few cognitive architectures that somewhat resemble SOAR and can be placed categorically on the same track. These include ICARUS, which has a strong emphasis on embodiment and has shown promise in terms of generality in a number of toy problems such as in-city driving, and LIDA which was developed for the US Navy to automatically organize and negotiate assignments with sailors but does not have embodiment as a design goal. As in SOAR, both of these implement different types of memory in specialized components and have a step-locked decision cycle. \\ \\ REF: Helgason, H.P. (2013). [[http://www.ru.is/media/td/Helgi_Pall_Helgason_PhD_CS_HR.pdf|General Attention Mechanisms for Artificial Intelligence Systems]]. Ph.D. Thesis, School of Computer Science, Reykjavik U. | |
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| ====Features of SOAR==== | =====Features of SOAR===== |
| | Predictable Robustness in Novel Circumstances | Not really | Since SOAR isn't really designed to operate and learn in novel circumstances, but rather work under variations of what it already knows, this issue hardly comes up. | | | Predictable Robustness in Novel Circumstances | Not really | Since SOAR isn't really designed to operate and learn in novel circumstances, but rather work under variations of what it already knows, this issue hardly comes up. | |
| | Graceful Degradation | No | The knowledge representation of SOAR is not organized around safe, predictable, or trustworthy operation (SOAR is an //early// experimental architecture). Since SOAR cannot do reflection, and SOAR doesn't learn, there is no way for SOAR to get better about evaluating its own performance over time, with experience. | | | Graceful Degradation | No | The knowledge representation of SOAR is not organized around safe, predictable, or trustworthy operation (SOAR is an //early// experimental architecture). Since SOAR cannot do reflection, and SOAR doesn't learn, there is no way for SOAR to get better about evaluating its own performance over time, with experience. | |
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| ====Non-Axiomatic Reasoning System (NARS)==== | =====Non-Axiomatic Reasoning System (NARS)===== |
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| | What it is | A reasoning system for handling complex unknown knowledge, based on non-axiomatic knowledge learned from experience. | | | What it is | A reasoning system for handling complex unknown knowledge, based on non-axiomatic knowledge learned from experience. | |
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| ====Features of NARS==== | =====Features of NARS===== |
| | Predictable Robustness in Novel Circumstances | \\ Yes | NARS is explicitly designed to operate and learn novel things in novel circumstances. It is the only architecture (besides AERA) that is directly based on, and specifically designed to, an **assumption of insufficient knowledge and resources** (AIKR). | | | Predictable Robustness in Novel Circumstances | \\ Yes | NARS is explicitly designed to operate and learn novel things in novel circumstances. It is the only architecture (besides AERA) that is directly based on, and specifically designed to, an **assumption of insufficient knowledge and resources** (AIKR). | |
| | \\ Graceful Degradation | \\ Yes | While the knowledge representation of NARS is not specifically aimed at achieving safe, predictable, or trustworthy operation, NARS can do reflection, so NARS could learn to get better about evaluating its own performance over time, which means it would be increasingly knowledgeable about its failure modes, making it increasingly more likley to fail gracefully. | | | \\ Graceful Degradation | \\ Yes | While the knowledge representation of NARS is not specifically aimed at achieving safe, predictable, or trustworthy operation, NARS can do reflection, so NARS could learn to get better about evaluating its own performance over time, which means it would be increasingly knowledgeable about its failure modes, making it increasingly more likley to fail gracefully. | |
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| ====AERA==== | =====AERA===== |
| | Description | The Auto-Catalytic Endogenous Reflective Architecture is an AGI-aspiring self-programming system that combines reactive, predictive and reflective control in a model-based and model-driven system that is programmed with a seed. | | | Description | The Auto-Catalytic Endogenous Reflective Architecture is an AGI-aspiring self-programming system that combines reactive, predictive and reflective control in a model-based and model-driven system that is programmed with a seed. | |
| | {{/public:t-720-atai:aera-high-level-2018.png?600}} || | | {{/public:t-720-atai:aera-high-level-2018.png?600}} || |
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| ====Features of AERA==== | =====Features of AERA===== |
| | Predictable Robustness in Novel Circumstances | \\ Yes | \\ Since AERA's learning is goal driven, its target operational environment are (semi-)novel circumstances. | | | Predictable Robustness in Novel Circumstances | \\ Yes | \\ Since AERA's learning is goal driven, its target operational environment are (semi-)novel circumstances. | |
| | Graceful Degradation | Yes | Knowledge representation in AERA is based around causal relations, which are essential for mapping out "how the world works". Because AERA's knowledge processing is organized around goals, with increased knowledge AERA will get closer and closer to "perfect operation" (i.e. meeting its top-level drives/goals, for which each instance was created). Furthermore, AERA can do reflection, so it gets better at evaluating its own performance over time, meaning it makes (causal) models of its own failure modes, increasing its chances of graceful degradation. | | | Graceful Degradation | Yes | Knowledge representation in AERA is based around causal relations, which are essential for mapping out "how the world works". Because AERA's knowledge processing is organized around goals, with increased knowledge AERA will get closer and closer to "perfect operation" (i.e. meeting its top-level drives/goals, for which each instance was created). Furthermore, AERA can do reflection, so it gets better at evaluating its own performance over time, meaning it makes (causal) models of its own failure modes, increasing its chances of graceful degradation. | |
