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LIDA (認知架構)：修订间差异

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=== 计算机制 ===
=== 计算机制 ===
LIDA架構采用了數個從“ New AI”中提取計算机制並進行設計的模块，这些模块包括各种[[模仿者（软件） |Copycat架構]] 、<ref>Hofstadter, D. (1995). Fluid Concepts and Creative Analogies: Computer Models of the Fundamental Mechanisms of Thought. New York: Basic Books.</ref><ref>Marshall, J. (2002). [http://science.slc.edu/~jmarshall/metacat/dissertation.pdf Metacat: A self-watching cognitive architecture for analogy-making]. In W. D. Gray & C. D. Schunn (eds.), Proceedings of the 24th Annual Conference of the Cognitive Science Society, pp. 631-636. Mahwah, NJ: Lawrence Erlbaum Associates</ref> [[稀疏分布式存储器|稀疏分布式内存]]、<ref>Kanerva, P. (1988). [https://www.cs.hmc.edu/~jpadgett/nnfinal/NNPrsntnJP1.pdf Sparse Distributed Memory]. Cambridge MA: The MIT Press</ref><ref>Rao, R. P. N., & Fuentes, O. (1998). [http://www.cs.utep.edu/ofuentes/raoML98.pdf Hierarchical Learning of Navigational Behaviors in an Autonomous Robot using a Predictive Sparse Distributed Memory]. Machine Learning, 31, 87-113</ref>基模机制、<ref>Drescher, G.L. (1991). [https://books.google.com/books?id=jYsEzeKHLNUC&printsec=frontcover#v=onepage&q&f=false Made-up minds: A Constructivist Approach to Artificial Intelligence]</ref><ref>Chaput, H. H., Kuipers, B., & Miikkulainen, R. (2003). [http://www.cs.utexas.edu/users/ai-lab/pubs/chaput.wsom03.pdf Constructivist Learning: A Neural Implementation of the Schema Mechanism]. Paper presented at the Proceedings of WSOM '03: Workshop for Self-Organizing Maps, Kitakyushu, Japan</ref> 行為網路， <ref>Maes, P. 1989. How to do the right thing. Connection Science 1:291-323</ref><ref>Tyrrell, T. (1994). [http://journals.sagepub.com/doi/abs/10.1177/105971239400200401 An Evaluation of Maes's Bottom-Up Mechanism for Behavior Selection]. Adaptive Behavior, 2, 307-348</ref>和[[包容體系結構|包容式架构]] 。 <ref>Brooks, R.A. Intelligence without Representation. Artificial intelligence, 1991. Elsevier</ref>
LIDA架構采用了數個從“ New AI”中提取計算机制並進行設計的模块，这些模块包括各种[[模仿者（软件） |Copycat架構]] 、<ref>Hofstadter, D. (1995). Fluid Concepts and Creative Analogies: Computer Models of the Fundamental Mechanisms of Thought. New York: Basic Books.</ref><ref>Marshall, J. (2002). [http://science.slc.edu/~jmarshall/metacat/dissertation.pdf Metacat: A self-watching cognitive architecture for analogy-making]. In W. D. Gray & C. D. Schunn (eds.), Proceedings of the 24th Annual Conference of the Cognitive Science Society, pp. 631-636. Mahwah, NJ: Lawrence Erlbaum Associates</ref> [[稀疏分布式存储器|稀疏分布式内存]]、<ref>Kanerva, P. (1988). [https://www.cs.hmc.edu/~jpadgett/nnfinal/NNPrsntnJP1.pdf Sparse Distributed Memory]. Cambridge MA: The MIT Press</ref><ref>Rao, R. P. N., & Fuentes, O. (1998). [http://www.cs.utep.edu/ofuentes/raoML98.pdf Hierarchical Learning of Navigational Behaviors in an Autonomous Robot using a Predictive Sparse Distributed Memory] {{Wayback|url=http://www.cs.utep.edu/ofuentes/raoML98.pdf |date=20170810103354 }}. Machine Learning, 31, 87-113</ref>基模机制、<ref>Drescher, G.L. (1991). [https://books.google.com/books?id=jYsEzeKHLNUC&printsec=frontcover#v=onepage&q&f=false Made-up minds: A Constructivist Approach to Artificial Intelligence]</ref><ref>Chaput, H. H., Kuipers, B., & Miikkulainen, R. (2003). [http://www.cs.utexas.edu/users/ai-lab/pubs/chaput.wsom03.pdf Constructivist Learning: A Neural Implementation of the Schema Mechanism]. Paper presented at the Proceedings of WSOM '03: Workshop for Self-Organizing Maps, Kitakyushu, Japan</ref> 行為網路， <ref>Maes, P. 1989. How to do the right thing. Connection Science 1:291-323</ref><ref>Tyrrell, T. (1994). [http://journals.sagepub.com/doi/abs/10.1177/105971239400200401 An Evaluation of Maes's Bottom-Up Mechanism for Behavior Selection]. Adaptive Behavior, 2, 307-348</ref>和[[包容體系結構|包容式架构]] 。 <ref>Brooks, R.A. Intelligence without Representation. Artificial intelligence, 1991. Elsevier</ref>


=== 心理学和神经生物学基础 ===
=== 心理学和神经生物学基础 ===

2020年2月15日 (六) 07:03的版本

LIDA （ 学习型智能分配代理 ） 認知架構是一个整合人工认知系统 ，试图從低層次的知覺動作到高層次的推理，对生物系统中「廣效的认知」進行建模。 LIDA架构以认知科学和认知神经科学為經驗基礎，主要由孟菲斯大学的斯坦·富蘭克林(Stan Franklin)與其同事所开发。除了提供假設來指導進一步的研究之外，該架構也支持软件代理與机器人的控制结构。 LIDA概念模型为许多认知过程提供了合理的解释，同時也作为思考心智如何運作的工具。

LIDA架構及其對應的概念模型有兩個假設：

(1)人類的許多認知功能藉著「認知循環」，即在意識內容、各种記憶系統和動作選擇之間頻繁迭代(~ 10hz)的交互作用所實現。

(2)這些認知循環就像認知的“原子”，構成了更高層次的認知歷程。

綜述

LIDA既不是符號方法，也不是严格的联結主义，而是一种混合架構，采用了多种计算机制，而这些计算机制是根据其心理合理性而选择的。LIDA認知循環是由採用這些机制的模塊和歷程所組成。

计算机制

LIDA架構采用了數個從“ New AI”中提取計算机制並進行設計的模块，这些模块包括各种Copycat架構 、[1][2] 稀疏分布式内存、[3][4]基模机制、[5][6] 行為網路， [7][8]和包容式架构 。 [9]

心理学和神经生物学基础

作为一种綜合性的概念计算认知架构，LIDA架构打算为人类大部分的認知功能建模。 [10][11]LIDA架構由各式各樣的認知模組和歷程所组成，試圖實現和具體化一些心理學和神經心理學理論，包括全球工作空间理论 、[12] 情境認知 、[13]知覺符號系統、[14] 工作记忆、[15]按能供性的记忆、[16][17]长期工作记忆[18]和H-CogAff架構。 [19]

LIDA的認知循環

LIDA认知循環可分为三个阶段：「理解阶段」、「注意（意识）阶段」以及「动作选择和学习阶段」。首先是從「理解阶段」開始，輸入的刺激會激活感觉记忆中的低级特征检测器，輸出則涉及到知覺聯想記憶，使得更加抽象的實體被輸入到其高級特征檢測器，如目標、類別、動作、事件等。由此產生的知覺會被移至工作空間，並在那里插入暫態的情節記憶和陳述性記憶以產生局部關聯，而這些局部關聯會與知覺相結合，生成當前的情境模型，這也就是代理對當前所發生事情的理解。「注意阶段」始于当前情境模型中最显着部分之結合，這樣的結合會去競爭一個當前意識內容中的地方「注意力」。接著，這些有意識的內容會被全域廣播，並進入「學習和動作選擇階段」。當有意識的廣播到達各种形式的記憶(知覺、情節和程序)時，新實體、新關聯以及對舊實體、關聯的強化就會出現。在進行這些學習以及使用意識內容的同時，合适的動作基模會從程序記憶中被實例化，并發送到動作選擇中，並在那里競相追求成為這個認知循環所選擇的行為。而所選定的行為會觸發感覺-運動記憶，以產生適合其執行的算法，從而完成認知循環。

历史

Virtual Mattie（V-Mattie）是一个软件代理[20] ，可以从從研討會組織者那里收集信息，撰写下週研討會的公告，并且每週定期将其邮寄到持續更新的列表中，而无需人工监督。 [21] V-Mattie使用了多項前述的计算机制。

巴爾斯 (Baars)的全球工作空间理论 （GWT）激发了V-Mattie，使其轉換成Conscious Mattie，Conscious Mattie是具有相同领域和任务的软件代理，其架构包括GWT意识机制。 Conscious Mattie是第一个在功能上（尽管不是很明显）具有意识的软件代理。Conscious Mattie促成了IDA。

IDA（智能分配代理）由美国海军 [22][23][24]所開發，用于完成被稱為「人事调配军官」的人力資源任务。在每个水手的任务结束时，将會被分配到一个新的营舍。此分配过程称为分发。海軍僱傭了近300名全職的人事调配军官來執行這些新任務。 IDA的任务是通过對人事调配军官的角色的自动化来促进此过程。 IDA经过前人事调配军官的测试，并被海军接受。各种海軍机构為IDA項目提供了大約150万美元的資助。

LIDA（學習型IDA）架構最初是通过添加几种学习风格和模式[25][26][27]从IDA衍生而来的，但此后已发展成为一个更大、更通用的软件框架。 [28][29]

脚注

  1. ^ Hofstadter, D. (1995). Fluid Concepts and Creative Analogies: Computer Models of the Fundamental Mechanisms of Thought. New York: Basic Books.
  2. ^ Marshall, J. (2002). Metacat: A self-watching cognitive architecture for analogy-making. In W. D. Gray & C. D. Schunn (eds.), Proceedings of the 24th Annual Conference of the Cognitive Science Society, pp. 631-636. Mahwah, NJ: Lawrence Erlbaum Associates
  3. ^ Kanerva, P. (1988). Sparse Distributed Memory. Cambridge MA: The MIT Press
  4. ^ Rao, R. P. N., & Fuentes, O. (1998). Hierarchical Learning of Navigational Behaviors in an Autonomous Robot using a Predictive Sparse Distributed Memory （页面存档备份，存于互联网档案馆）. Machine Learning, 31, 87-113
  5. ^ Drescher, G.L. (1991). Made-up minds: A Constructivist Approach to Artificial Intelligence
  6. ^ Chaput, H. H., Kuipers, B., & Miikkulainen, R. (2003). Constructivist Learning: A Neural Implementation of the Schema Mechanism. Paper presented at the Proceedings of WSOM '03: Workshop for Self-Organizing Maps, Kitakyushu, Japan
  7. ^ Maes, P. 1989. How to do the right thing. Connection Science 1:291-323
  8. ^ Tyrrell, T. (1994). An Evaluation of Maes's Bottom-Up Mechanism for Behavior Selection. Adaptive Behavior, 2, 307-348
  9. ^ Brooks, R.A. Intelligence without Representation. Artificial intelligence, 1991. Elsevier
  10. ^ Franklin, S., & Patterson, F. G. J. (2006). The LIDA Architecture: Adding New Modes of Learning to an Intelligent, Autonomous, Software Agent IDPT-2006 Proceedings (Integrated Design and Process Technology): Society for Design and Process Science
  11. ^ Franklin, S., Ramamurthy, U., D'Mello, S., McCauley, L., Negatu, A., Silva R., & Datla, V. (2007). LIDA: A computational model of global workspace theory and developmental learning. In AAAI Fall Symposium on AI and Consciousness: Theoretical Foundations and Current Approaches. Arlington, VA: AAAI
  12. ^ Baars, B. J. (1988). A cognitive theory of consciousness. Cambridge: Cambridge University Press
  13. ^ Varela, F. J., Thompson, E., & Rosch, E. (1991). The Embodied Mind. Cambridge, Massachusetts: MIT Press
  14. ^ Barsalou, L. W. 1999. Perceptual symbol systems. Behavioral and Brain Sciences 22:577–609. MA: The MIT Press
  15. ^ Baddeley, A. D., & Hitch, G. J. (1974). Working memory. In G. A. Bower (Ed.), The Psychology of Learning and Motivation (pp. 47–89). New York: Academic Press
  16. ^ Ericsson, K. A., and W. Kintsch. 1995. Long-term working memory. Psychological Review 102:21–245
  17. ^ Glenberg, A. M. 1997. What memory is for. Behavioral and Brain Sciences 20:1–19
  18. ^ Ericsson, K. A., and W. Kintsch. 1995. Long-term working memory. Psychological Review 102:21–245
  19. ^ Sloman, A. 1999. What Sort of Architecture is Required for a Human-like Agent? In Foundations of Rational Agency, ed. M. Wooldridge, and A. Rao. Dordrecht, Netherlands: Kluwer Academic Publishers
  20. ^ Franklin, S., & Graesser, A., 1997. Is it an Agent, or just a Program?: A Taxonomy for Autonomous Agents. Proceedings of the Third International Workshop on Agent Theories, Architectures, and Languages, published as Intelligent Agents III, Springer-Verlag, 1997, 21-35
  21. ^ Franklin, S., Graesser, A., Olde, B., Song, H., & Negatu, A. (1996, Nov). Virtual Mattie—an Intelligent Clerical Agent. Paper presented at the Symposium on Embodied Cognition and Action: AAAI, Cambridge, Massachusetts.
  22. ^ Franklin, S., Kelemen, A., & McCauley, L. (1998). IDA: A Cognitive Agent Architecture IEEE Conf on Systems, Man and Cybernetics (pp. 2646–2651 ): IEEE Press
  23. ^ Franklin, S. (2003). IDA: A Conscious Artifact? Journal of Consciousness Studies, 10, 47–66
  24. ^ Franklin, S., & McCauley, L. (2003). Interacting with IDA. In H. Hexmoor, C. Castelfranchi & R. Falcone (Eds.), Agent Autonomy (pp. 159–186 ). Dordrecht: Kluwer
  25. ^ D'Mello, Sidney K., Ramamurthy, U., Negatu, A., & Franklin, S. (2006). A Procedural Learning Mechanism for Novel Skill Acquisition. In T. Kovacs & James A. R. Marshall (Eds.), Proceeding of Adaptation in Artificial and Biological Systems, AISB'06 (Vol. 1, pp. 184–185). Bristol, England: Society for the Study of Artificial Intelligence and the Simulation of Behaviour
  26. ^ Franklin, S. (2005, March 21–23, 2005). Perceptual Memory and Learning: Recognizing, Categorizing, and Relating. Paper presented at the Symposium on Developmental Robotics: American Association for Artificial Intelligence (AAAI), Stanford University, Palo Alto CA, USA
  27. ^ Franklin, S., & Patterson, F. G. J. (2006). The LIDA Architecture: Adding New Modes of Learning to an Intelligent, Autonomous, Software Agent IDPT-2006 Proceedings (Integrated Design and Process Technology): Society for Design and Process Science
  28. ^ Franklin, S., & McCauley, L. (2004). Feelings and Emotions as Motivators and Learning Facilitators Architectures for Modeling Emotion: Cross-Disciplinary Foundations, AAAI 2004 Spring Symposium Series (Vol. Technical Report SS-04-02 pp. 48–51). Stanford University, Palo Alto, California, USA: American Association for Artificial Intelligence
  29. ^ Negatu, A., D'Mello, Sidney K., & Franklin, S. (2007). Cognitively Inspired Anticipation and Anticipatory Learning Mechanisms for Autonomous Agents. In M. V. Butz, O. Sigaud, G. Pezzulo & G. O. Baldassarre (Eds.), Proceedings of the Third Workshop on Anticipatory Behavior in Adaptive Learning Systems (ABiALS 2006) (pp. 108-127). Rome, Italy: Springer Verlag

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