LIDA (cognitive architecture)

Last updated

The LIDA (Learning Intelligent Distribution Agent) cognitive architecture attempts to model a broad spectrum of cognition in biological systems, from low-level perception/action to high-level reasoning. Developed primarily by Stan Franklin and colleagues at the University of Memphis, the LIDA architecture is empirically grounded in cognitive science and cognitive neuroscience. It is an extension of IDA, which adds mechanisms for learning. [1] In addition to providing hypotheses to guide further research, the architecture can support control structures for software agents and robots. Providing plausible explanations for many cognitive processes, the LIDA conceptual model is also intended as a tool with which to think about how minds work.

Contents

Two hypotheses underlie the LIDA architecture and its corresponding conceptual model: 1) Much of human cognition functions by means of frequently iterated (~10 Hz) interactions, called cognitive cycles, between conscious contents, the various memory systems and action selection. 2) These cognitive cycles, serve as the "atoms" of cognition of which higher-level cognitive processes are composed.

Overview

Though it is neither symbolic nor strictly connectionist, LIDA is a hybrid architecture in that it employs a variety of computational mechanisms, chosen for their psychological plausibility. The LIDA cognitive cycle is composed of modules and processes employing these mechanisms.

Computational mechanisms

The LIDA architecture uses several modules, [2] including variants of the Copycat Architecture, [3] [4] sparse distributed memory, [5] [6] the schema mechanism, [7] [8] the Behavior Net, [9] [10] and the subsumption architecture. [11]

Psychological and neurobiological underpinnings

As a comprehensive, conceptual and computational cognitive architecture the LIDA architecture is intended to model a large portion of human cognition. [12] [13] Comprising a broad array of cognitive modules and processes, the LIDA architecture attempts to implement and flesh out a number of psychological and neuropsychological theories including Global Workspace Theory, [14] situated cognition, [15] perceptual symbol systems, [16] working memory, [17] memory by affordances, [18] long-term working memory, [19] and the H-CogAff architecture. [20]

Codelets

LIDA relies heavily on what Franklin calls codelets. A codelet is a "special purpose, relatively independent, mini-agent typically implemented as a small piece of code running as a separate thread." [21]

Cognitive cycle

The LIDA cognitive cycle can be subdivided into three phases: understanding, consciousness, and action selection (which includes learning). [2]

In the understanding phase, incoming stimuli activate low-level feature detectors in sensory memory. The output engages perceptual associative memory where higher-level feature detectors feed in to more abstract entities such as objects, categories, actions, events, etc. The resulting percept moves to the Workspace where it cues both Transient Episodic Memory and Declarative Memory producing local associations. These local associations are combined with the percept to generate a current situational model which is the agent's understanding of what is going on right now. [2]

In the consciousness phase, "attention codelets" form coalitions by selecting portions of the situational model and moving them to the Global Workspace. These coalitions then compete for attention. The winning coalition becomes the content of consciousness and is broadcast globally. [2]

These conscious contents are then broadcast globally, initiating the learning and action selection phase. New entities and associations, and the reinforcement of old ones, occur as the conscious broadcast reaches the various forms of memory, perceptual, episodic and procedural. In parallel with all this learning, and using the conscious contents, possible action schemes are instantiated from Procedural Memory and sent to Action Selection, where they compete to be the behavior selected for this cognitive cycle. The selected behavior triggers sensory-motor memory to produce a suitable algorithm for its execution, which completes the cognitive cycle. [2]

This process repeats continuously, with each cycle representing a cognitive "moment" that contributes to higher-level cognitive processes. [2]

History

Virtual Mattie (V-Mattie) is a software agent [22] that gathers information from seminar organizers, composes announcements of next week's seminars, and mails them each week to a list that it keeps updated, all without the supervision of a human. [23] V-Mattie employed many of the computational mechanisms mentioned above.

Baars' Global Workspace Theory (GWT) inspired the transformation of V-Mattie into Conscious Mattie, a software agent with the same domain and tasks whose architecture included a consciousness mechanism à la GWT. Conscious Mattie was the first functionally, though not phenomenally, conscious software agent. Conscious Mattie gave rise to IDA.

IDA (Intelligent Distribution Agent) was developed for the US Navy [24] [25] [26] to fulfill tasks performed by human resource personnel called detailers. At the end of each sailor's tour of duty, he or she is assigned to a new billet. This assignment process is called distribution. The Navy employs almost 300 full time detailers to effect these new assignments. IDA's task is to facilitate this process, by automating the role of detailer. IDA was tested by former detailers and accepted by the Navy. Various Navy agencies supported the IDA project to the tune of some $1,500,000.

The LIDA (Learning IDA) architecture was originally spawned from IDA by the addition of several styles and modes of learning, [27] [28] [29] but has since then grown to become a much larger and generic software framework. [30] [31]

Footnotes

  1. ↑ "Projects: IDA and LIDA". University of Memphis. Retrieved 2024-08-25.
  2. 1 2 3 4 5 6 J. Baars, Bernard; Franklin, Stan (2009). "Consciousness is computational: The Lida model of global workspace theory". International Journal of Machine Consciousness.
  3. ↑ Hofstadter, D. (1995). Fluid Concepts and Creative Analogies: Computer Models of the Fundamental Mechanisms of Thought. New York: Basic Books.
  4. ↑ 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
  5. ↑ Kanerva, P. (1988). Sparse Distributed Memory. Cambridge MA: The MIT Press
  6. ↑ Rao, R. P. N., & Fuentes, O. (1998). Hierarchical Learning of Navigational Behaviors in an Autonomous Robot using a Predictive Sparse Distributed Memory Archived 2017-08-10 at the Wayback Machine . Machine Learning, 31, 87-113
  7. ↑ Drescher, G.L. (1991). Made-up minds: A Constructivist Approach to Artificial Intelligence
  8. ↑ 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
  9. ↑ Maes, P. 1989. How to do the right thing. Connection Science 1:291-323
  10. ↑ Tyrrell, T. (1994). An Evaluation of Maes's Bottom-Up Mechanism for Behavior Selection. Adaptive Behavior, 2, 307-348
  11. ↑ Brooks, R.A. Intelligence without Representation. Artificial intelligence, 1991. Elsevier
  12. ↑ 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
  13. ↑ 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
  14. ↑ Baars, B. J. (1988). A cognitive theory of consciousness. Cambridge: Cambridge University Press
  15. ↑ Varela, F. J., Thompson, E., & Rosch, E. (1991). The Embodied Mind. Cambridge, Massachusetts: MIT Press
  16. ↑ Barsalou, L. W. 1999. Perceptual symbol systems. Behavioral and Brain Sciences 22:577–609. MA: The MIT Press
  17. ↑ 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
  18. ↑ Glenberg, A. M. 1997. What memory is for. Behavioral and Brain Sciences 20:1–19
  19. ↑ Ericsson, K. A., and W. Kintsch. 1995. Long-term working memory. Psychological Review 102:21–245
  20. ↑ 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
  21. ↑ Franklin, Stan (January 2003). "IDA: A conscious artifact?". Journal of Consciousness Studies.
  22. ↑ 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
  23. ↑ 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.
  24. ↑ Franklin, S., Kelemen, A., & McCauley, L. (1998). IDA: A Cognitive Agent Architecture IEEE Conf on Systems, Man and Cybernetics (pp. 2646–2651 ): IEEE Press
  25. ↑ Franklin, S. (2003). IDA: A Conscious Artifact? Journal of Consciousness Studies, 10, 47–66
  26. ↑ Franklin, S., & McCauley, L. (2003). Interacting with IDA. In H. Hexmoor, C. Castelfranchi & R. Falcone (Eds.), Agent Autonomy (pp. 159–186 ). Dordrecht: Kluwer
  27. ↑ 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
  28. ↑ 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
  29. ↑ 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
  30. ↑ 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
  31. ↑ 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

Related Research Articles

<span class="mw-page-title-main">Cognitive science</span> Interdisciplinary scientific study of cognitive processes

Cognitive science is the interdisciplinary, scientific study of the mind and its processes. It examines the nature, the tasks, and the functions of cognition. Mental faculties of concern to cognitive scientists include language, perception, memory, attention, reasoning, and emotion; to understand these faculties, cognitive scientists borrow from fields such as linguistics, psychology, artificial intelligence, philosophy, neuroscience, and anthropology. The typical analysis of cognitive science spans many levels of organization, from learning and decision to logic and planning; from neural circuitry to modular brain organization. One of the fundamental concepts of cognitive science is that "thinking can best be understood in terms of representational structures in the mind and computational procedures that operate on those structures."

Artificial consciousness, also known as machine consciousness, synthetic consciousness, or digital consciousness, is the consciousness hypothesized to be possible in artificial intelligence. It is also the corresponding field of study, which draws insights from philosophy of mind, philosophy of artificial intelligence, cognitive science and neuroscience. The same terminology can be used with the term "sentience" instead of "consciousness" when specifically designating phenomenal consciousness.

A cognitive model is a representation of one or more cognitive processes in humans or other animals for the purposes of comprehension and prediction. There are many types of cognitive models, and they can range from box-and-arrow diagrams to a set of equations to software programs that interact with the same tools that humans use to complete tasks. In terms of information processing, cognitive modeling is modeling of human perception, reasoning, memory and action.

Soar is a cognitive architecture, originally created by John Laird, Allen Newell, and Paul Rosenbloom at Carnegie Mellon University.

<span class="mw-page-title-main">ACT-R</span>

ACT-R is a cognitive architecture mainly developed by John Robert Anderson and Christian Lebiere at Carnegie Mellon University. Like any cognitive architecture, ACT-R aims to define the basic and irreducible cognitive and perceptual operations that enable the human mind. In theory, each task that humans can perform should consist of a series of these discrete operations.

<span class="mw-page-title-main">Stan Franklin</span> American scientist (1931–2023)

Stan Franklin was an American scientist. He was the W. Harry Feinstone Interdisciplinary Research Professor at the University of Memphis in Memphis, Tennessee, and co-director of the Institute of Intelligent Systems. He is the author of Artificial Minds, and the developer of IDA and its successor LIDA, both computational implementations of Global Workspace Theory. He is founder of the Cognitive Computing Research Group at the University of Memphis.

Global workspace theory (GWT) is a framework for thinking about consciousness proposed by cognitive scientists Bernard Baars and Stan Franklin in the late 1980s. It was developed to qualitatively explain a large set of matched pairs of conscious and unconscious processes. GWT has been influential in modeling consciousness and higher-order cognition as emerging from competition and integrated flows of information across widespread, parallel neural processes.

A cognitive architecture refers to both a theory about the structure of the human mind and to a computational instantiation of such a theory used in the fields of artificial intelligence (AI) and computational cognitive science. These formalized models can be used to further refine comprehensive theories of cognition and serve as the frameworks for useful artificial intelligence programs. Successful cognitive architectures include ACT-R and SOAR. The research on cognitive architectures as software instantiation of cognitive theories was initiated by Allen Newell in 1990.

<span class="mw-page-title-main">Copycat (software)</span> AI software

Copycat is a model of analogy making and human cognition based on the concept of the parallel terraced scan, developed in 1988 by Douglas Hofstadter, Melanie Mitchell, and others at the Center for Research on Concepts and Cognition, Indiana University Bloomington. The original Copycat was written in Common Lisp and is bitrotten ; however, Java and Python ports exist. The latest version in 2018 is a Python3 port by Lucas Saldyt and J. Alan Brogan.

<span class="mw-page-title-main">Stephen Grossberg</span> American scientist (born 1939)

Stephen Grossberg is a cognitive scientist, theoretical and computational psychologist, neuroscientist, mathematician, biomedical engineer, and neuromorphic technologist. He is the Wang Professor of Cognitive and Neural Systems and a Professor Emeritus of Mathematics & Statistics, Psychological & Brain Sciences, and Biomedical Engineering at Boston University.

Cognitive Robotics or Cognitive Technology is a subfield of robotics concerned with endowing a robot with intelligent behavior by providing it with a processing architecture that will allow it to learn and reason about how to behave in response to complex goals in a complex world. Cognitive robotics may be considered the engineering branch of embodied cognitive science and embodied embedded cognition, consisting of Robotic Process Automation, Artificial Intelligence, Machine Learning, Deep Learning, Optical Character Recognition, Image Processing, Process Mining, Analytics, Software Development and System Integration.

<span class="mw-page-title-main">CLARION (cognitive architecture)</span>

Connectionist Learning with Adaptive Rule Induction On-line (CLARION) is a computational cognitive architecture that has been used to simulate many domains and tasks in cognitive psychology and social psychology, as well as implementing intelligent systems in artificial intelligence applications. An important feature of CLARION is the distinction between implicit and explicit processes and focusing on capturing the interaction between these two types of processes. The system was created by the research group led by Ron Sun.

Psi-theory, developed by Dietrich Dörner at the University of Bamberg, is a systemic psychological theory covering human action regulation, intention selection and emotion. It models the human mind as an information processing agent, controlled by a set of basic physiological, social and cognitive drives. Perceptual and cognitive processing are directed and modulated by these drives, which allow the autonomous establishment and pursuit of goals in an open environment.

<span class="mw-page-title-main">Neural correlates of consciousness</span> Neuronal events sufficient for a specific conscious percept

The neural correlates of consciousness (NCC) are the minimal set of neuronal events and mechanisms sufficient for the occurrence of the mental states to which they are related. Neuroscientists use empirical approaches to discover neural correlates of subjective phenomena; that is, neural changes which necessarily and regularly correlate with a specific experience. The set should be minimal because, under the materialist assumption that the brain is sufficient to give rise to any given conscious experience, the question is which of its components are necessary to produce it.

Allan M. Collins is an American cognitive scientist, Professor Emeritus of Learning Sciences at Northwestern University's School of Education and Social Policy. His research is recognized as having broad impact on the fields of cognitive psychology, artificial intelligence, and education.

In artificial intelligence research, the situated approach builds agents that are designed to behave effectively successfully in their environment. This requires designing AI "from the bottom-up" by focussing on the basic perceptual and motor skills required to survive. The situated approach gives a much lower priority to abstract reasoning or problem-solving skills.

The Troland Research Awards are an annual prize given by the United States National Academy of Sciences to two researchers in recognition of psychological research on the relationship between consciousness and the physical world. The areas where these award funds are to be spent include but are not limited to areas of experimental psychology, the topics of sensation, perception, motivation, emotion, learning, memory, cognition, language, and action. The award preference is given to experimental work with a quantitative approach or experimental research seeking physiological explanations.

<span class="mw-page-title-main">Embodied cognition</span> Interdisciplinary theory

Embodied cognition is the concept suggesting that many features of cognition are shaped by the state and capacities of the organism. The cognitive features include a wide spectrum of cognitive functions, such as perception biases, memory recall, comprehension and high-level mental constructs and performance on various cognitive tasks. The bodily aspects involve the motor system, the perceptual system, the bodily interactions with the environment (situatedness), and the assumptions about the world built the functional structure of organism's brain and body.

William J. Clancey is an American computer scientist who specializes in cognitive science and artificial intelligence. He has worked in computing in a wide range of sectors, including medicine, education, and finance, and had performed research that brings together cognitive and social science to study work practices and examine the design of agent systems. Clancey has been described as having developed “some of the earliest artificial intelligence programs for explanation, the critiquing method of consultation, tutorial discourse, and student modeling,” and his research has been described as including “work practice modeling, distributed multiagent systems, and the ethnography of field science.” He has also participated in Mars Exploration Rover mission operations, “simulation of a day-in-the-life of the ISS, knowledge management for future launch vehicles, and developing flight systems that make automation more transparent.” Clancey’s work on "heuristic classification" and "model construction operators" is regarded as having been influential in the design of expert systems and instructional programs.

The Dehaene–Changeux model (DCM), also known as the global neuronal workspace, or global cognitive workspace model, is a part of Bernard Baars's global workspace model for consciousness.

This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.