Jump to content

Blackboard system

From Wikipedia, the free encyclopedia

This is an old revision of this page, as edited by Dsemeas (talk | contribs) at 20:16, 15 August 2019 (→Components). The present address (URL) is a permanent link to this revision, which may differ significantly from the current revision.

A blackboard system is an artificial intelligence approach based on the blackboard architectural model,[1][2][3][4] where a common knowledge base, the "blackboard", is iteratively updated by a diverse group of specialist knowledge sources, starting with a problem specification and ending with a solution. Each knowledge source updates the blackboard with a partial solution when its internal constraints match the blackboard state. In this way, the specialists work together to solve the problem. The blackboard model was originally designed as a way to handle complex, ill-defined problems, where the solution is the sum of its parts.

Metaphor

The following scenario provides a simple metaphor that gives some insight into how a blackboard functions:

A group of specialists are seated in a room with a large blackboard. They work as a team to brainstorm a solution to a problem, using the blackboard as the workplace for cooperatively developing the solution.

The session begins when the problem specifications are written onto the blackboard. The specialists all watch the blackboard, looking for an opportunity to apply their expertise to the developing solution. When someone writes something on the blackboard that allows another specialist to apply their expertise, the second specialist records their contribution on the blackboard, hopefully enabling other specialists to then apply their expertise. This process of adding contributions to the blackboard continues until the problem has been solved.

Components

A blackboard-system application consists of three major components

  1. The software specialist modules, which are called knowledge sources (KSs). Like the human experts at a blackboard, each knowledge source provides specific expertise needed by the application.
  2. The blackboard, a shared repository of problems, partial solutions, suggestions, and contributed information. The blackboard can be thought of as a dynamic "library" of contributions to the current problem that have been recently "published" by other knowledge sources.
  3. The control shell, which controls the flow of problem-solving activity in the system. Just as the eager human specialists need a moderator to prevent them from trampling each other in a mad dash to grab the chalk, KSs need a mechanism to organize their use in the most effective and coherent fashion. In a blackboard system, this is provided by the control shell.

Learnable Task Modeling Language

A blackboard system is the central space in a multi-agent system. It's used for describing the world as a communication platform for agents. To realize a blackboard in a computer program, a machine readable notation is needed in which facts can be stored. One attempt in doing so is a SQL database, another option is the Learnable Task Modeling Language (LTML). The syntax of the LTML planning language is similar to PDDL, but adds extra features like control structures and OWL-S models.[5][6] LTML was developed in 2007[7] as part of a much larger project called POIROT (Plan Order Induction by Reasoning from One Trial)[8], which is a Learning from demonstrations framework for process mining. In POIROT, Plan traces and hypotheses are stored in the LTML syntax for creating semantic web services.[9]

Here is a small example: A human user is executing a workflow in a computer game. He presses some buttons and interacts with the game engine. While he is doing so, a plan trace is created. That means, the user's actions are stored in a logfile. The logfile gets transformed into a machine readable notation which is enriched by semantic attributes. The result is a textfile in the LTML syntax which is put on the blackboard. Agents (software programs in the blackboard system) are able to parse the LTML syntax.

Implementations

Famous examples of early academic blackboard systems are the Hearsay II speech recognition system and Douglas Hofstadter's Copycat and Numbo projects.

More recent examples include deployed real-world applications, such as the PLAN component of the Mission Control System for RADARSAT-1,[10] an Earth observation satellite developed by Canada to monitor environmental changes and Earth's natural resources.

GTXImage CAD software by GTX Corporation was developed in the early 1990s using a set of rulebases and neural networks as specialists operating on a blackboard system.

Adobe Acrobat Capture (now discontinued) used a Blackboard system to decompose and recognize image pages to understand the objects, text, and fonts on the page. This function is currently built into the retail version of Adobe Acrobat as "OCR Text Recognition". Details of a similar OCR blackboard for Farsi text are in the public domain.[11]

Blackboard systems are used routinely in many military C4ISTAR systems for detecting and tracking objects.

Criticism

Blackboard systems were popular before the AI Winter and, along with most symbolic AI models, fell out of fashion during that period. Along with other models it was realised that initial successes on toy problems did not scale well to real problems on the available computers of the time. Most problems using blackboards are inherently NP-hard, so resist tractable solution by any algorithm in the large size limit. During the same period, statistical pattern recognition became dominant, most notably via simple Hidden Markov Models outperforming symbolic approaches such as Hearsay-II in the domain of speech recognition.

Recent developments

Blackboard-like systems have been constructed within modern Bayesian machine learning settings, using agents to add and remove Bayesian network nodes. In these 'Bayesian Blackboard' systems, the heuristics can acquire more rigorous probabilistic meanings as proposal and acceptances in Metropolis Hastings sampling though the space of possible structures.[12][13][14] Conversely, using these mappings, existing Metropolis-Hastings samplers over structural spaces may now thus be viewed as forms of blackboard systems even when not named as such by the authors. Such samplers are commonly found in musical transcription algorithms for example.[15]

Blackboard systems have also been used to build large-scale intelligent systems for the annotation of media content, automating parts of traditional social science research. In this domain, the problem of integrating various AI algorithms into a single intelligent system arises spontaneously, with blackboards providing a way for a collection of distributed, modular natural language processing algorithms to each annotate the data in a central space, without needing to coordinate their behavior.[16]

See also

References

  1. ^ Erman, L. D.; Hayes-Roth, F.; Lesser, V. R.; Reddy, D. R. (1980). "The Hearsay-II Speech-Understanding System: Integrating Knowledge to Resolve Uncertainty". ACM Computing Surveys. 12 (2): 213. doi:10.1145/356810.356816.
  2. ^ Corkill, Daniel D. (September 1991). "Blackboard Systems" (PDF). AI Expert. 6 (9): 40–47.
  3. ^ * Nii, H. Yenny (1986). Blackboard Systems (PDF) (Technical report). Department of Computer Science, Stanford University. STAN-CS-86-1123. Retrieved 2013-04-12.
  4. ^ Hayes-Roth, B. (1985). "A blackboard architecture for control". Artificial Intelligence. 26 (3): 251–321. doi:10.1016/0004-3702(85)90063-3.
  5. ^ Goldman, Robert P and Maraist, John (2010). Shopper: A System for Executing and Simulating Expressive Plans. ICAPS. pp. 230–233.{{cite conference}}: CS1 maint: multiple names: authors list (link)
  6. ^ Pechoucek, Michal (2010). Agent-Based Computing in Distributed Adversarial Planning (Technical report). Czech Technical Univ Prague.
  7. ^ Burstein, Mark and Brinn, Marshall and Cox, Mike and Hussain, Talib and Laddaga, Robert and McDermott, Drew and McDonald, David and Tomlinson, Ray (2007). An architecture and language for the integrated learning of demonstrations. AAAI Workshop Acquiring Planning Knowledge via Demonstration. pp. 6–11.{{cite conference}}: CS1 maint: multiple names: authors list (link)
  8. ^ Morrison, Clayton T and Cohen, Paul R (2007). Designing experiments to test planning knowledge about plan-step order constraints. ICAPS workshop on Intelligent Planning and Learning.{{cite conference}}: CS1 maint: multiple names: authors list (link)
  9. ^ Burstein, Mark and Bobrow, Robert and Ferguson, William and Laddaga, Robert and Robertson, Paul (2010). Learning from Observing: Vision and POIROT-Using Metareasoning for Self Adaptation. Self-Adaptive and Self-Organizing Systems Workshop (SASOW), 2010 Fourth IEEE International Conference on. pp. 300–307.{{cite conference}}: CS1 maint: multiple names: authors list (link)
  10. ^ Corkill, Daniel D. "Countdown to success: Dynamic objects, GBB, and RADARSAT-1." Communications of the ACM 40.5 (1997): 48-58.
  11. ^ Khosravi, H., & Kabir, E. (2009). A blackboard approach towards integrated Farsi OCR system. International Journal of Document Analysis and Recognition (IJDAR), 12(1), 21-32.
  12. ^ Fox C, Evans M, Pearson M, Prescott T (2011). "Towards hierarchical blackboard mapping on a whiskered robot" (PDF). Robotics and Autonomous Systems. 60 (11): 1356–66. doi:10.1016/j.robot.2012.03.005.
  13. ^ Sutton C. A Bayesian Blackboard for Information Fusion, Proc. Int. Conf. Information Fusion, 2004
  14. ^ Carver, Norman (May 1997). "A Revisionist View of Blackboard Systems". Proceedings of the 1997 Midwest Artificial Intelligence and Cognitive Science Society Conference. {{cite conference}}: Unknown parameter |booktitle= ignored (|book-title= suggested) (help)
  15. ^ Godsill, Simon, and Manuel Davy. "Bayesian harmonic models for musical pitch estimation and analysis." Acoustics, Speech, and Signal Processing (ICASSP), 2002 IEEE International Conference on. Vol. 2. IEEE, 2002.
  16. ^ Flaounas, Ilias; Lansdall-Welfare, Thomas; Antonakaki, Panagiota; Cristianini, Nello (2014-02-25). "The Anatomy of a Modular System for Media Content Analysis". arXiv:1402.6208 [cs.MA].
  • Open Blackboard System An open source framework for developing blackboard systems.
  • GBBopen An open source blackboard system framework for Common Lisp.
  • Blackboard Event Processor An open source blackboard implementation that runs on the JVM but supports plan scripting in JavaScript and JRuby.
  • KOGMO-RTDB A real-time open source blackboard for C/C++, used by some DARPA Urban Challenge autonomous vehicles.
  • HarTech Technologies A company that provides both Simulation and Command and Control solutions which are all based on a unique Blackboard architecture. The Blackboard development framework can be utilized to develop custom applications.
  • The BB1 Blackboard Control architecture An older Blackboard system, available for Common Lisp and C++.
  • Macsy A modular blackboard architecture for Python built on top of MongoDB for the annotation of media content.

Further reading

Retrieved from "https://en.wikipedia.org/enwiki/w/index.php?title=Blackboard_system&oldid=910981897"