Blackboard system explained

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. The user presses some buttons and interacts with the game engine. While the user interacts with the game, 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

We start by discussing two well known early blackboard systems, BB1 and GBB, below and then discuss more recent implementations and applications.

The BB1 blackboard architecture[10] was originally inspired by studies of how humans plan to perform multiple tasks in a trip, used task-planning as a simplified example of tactical planning for the Office of Naval Research.[11] Hayes-Roth & Hayes-Roth found that human planning was more closely modeled as an opportunistic process, in contrast to the primarily top-down planners used at the time:

A key innovation of BB1 was that it applied this opportunistic planning model to its own control, using the same blackboard model of incremental, opportunistic, problem-solving that was applied to solve domain problems. Meta-level reasoning with control knowledge sources could then monitor whether planning and problem-solving were proceeding as expected or stalled. If stalled, BB1 could switch from one strategy to another as conditions – such as the goals being considered or the time remaining – changed. BB1 was applied in multiple domains: construction site planning,[12] inferring 3-D protein structures from X-ray crystallography,[13] intelligent tutoring systems,[14] and real-time patient monitoring.[15]

BB1 also allowed domain-general language frameworks to be designed for wide classes of problems. For example, the ACCORD [16] language framework defined a particular approach to solving configuration problems. The problem-solving approach was to incrementally assemble a solution by adding objects and constraints, one at a time. Actions in the ACCORD language framework appear as short English-like commands or sentences for specifying preferred actions, events to trigger KSes, preconditions to run a KS action, and obviation conditions to discard a KS action that is no longer relevant.

GBB [17] focused on efficiency, in contrast to BB1, which focused more on sophisticated reasoning and opportunistic planning. GBB improves efficiency by allowing blackboards to be multi-dimensional, where dimensions can be either ordered or not, and then by increasing the efficiency of pattern matching. GBB1,[18] one of GBB's control shells implements BB1's style of control while adding efficiency improvements.

Other well-known of early academic blackboard systems are the Hearsay II speech recognition system and Douglas Hofstadter's Copycat and Numbo projects.

Some more recent examples of deployed real-world applications include:

Blackboard systems are used routinely in many military C4ISTAR systems for detecting and tracking objects. Another example of current use is in Game AI, where they are considered a standard AI tool to help with adding AI to video games.[21] [22]

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.[23] [24] [25] 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.[26]

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.[27]

See also

External links

Further reading

Notes and 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. 10.1145/356810.356816. 118556.
  2. Corkill . Daniel D. . Blackboard Systems . AI Expert . 6 . 9 . September 1991 . 40 - 47 .
    • H. Penny . Nii . Blackboard Systems . STAN-CS-86-1123 . Department of Computer Science, Stanford University . 1986 . 2013-04-12 .
  4. Hayes-Roth. B.. Barbara Hayes-Roth. 1985. A blackboard architecture for control. Artificial Intelligence. 26. 3. 251–321. 10.1016/0004-3702(85)90063-3.
  5. Shopper: A System for Executing and Simulating Expressive Plans . Goldman, Robert P and Maraist, John . ICAPS . 230–233 . 2010.
  6. Agent-Based Computing in Distributed Adversarial Planning . Pechoucek, Michal . 2010 . Czech Technical Univ Prague.
  7. An architecture and language for the integrated learning of demonstrations . Burstein, Mark and Brinn, Marshall and Cox, Mike and Hussain, Talib and Laddaga, Robert and McDermott, Drew and McDonald, David and Tomlinson, Ray . AAAI Workshop Acquiring Planning Knowledge via Demonstration . 6–11 . 2007.
  8. Designing experiments to test planning knowledge about plan-step order constraints . Morrison, Clayton T and Cohen, Paul R . ICAPS workshop on Intelligent Planning and Learning . 2007.
  9. Learning from Observing: Vision and POIROT-Using Metareasoning for Self Adaptation . Burstein, Mark and Bobrow, Robert and Ferguson, William and Laddaga, Robert and Robertson, Paul . Self-Adaptive and Self-Organizing Systems Workshop (SASOW), 2010 Fourth IEEE International Conference on . 300–307 . 2010.
  10. 10.1016/0004-3702(85)90063-3. 26. 3. 251–321. Hayes-Roth. Barbara. A blackboard architecture for control. Artificial Intelligence. 1985.
  11. RAND. Hayes-Roth. Barbara. Human Planning Processes. 1980.
  12. 2413-5844. Tommelein. Iris D.. Levitt. Raymond E.. Hayes-Roth. Barbara. Sightplan: An Artificial Intelligence Tool to Assist Construction Managers with Site Layout. Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC). Proceedings of the 6th International Symposium on Automation and Robotics in Construction (ISARC). 2023-03-17. 1989. 10.22260/ISARC1989/0043., inferring 3D
  13. AAAI. 904–909. Hayes-Roth. Barbara. Buchanan. Bruce G.. Lichtarge. Olivier. Hewitt. Mike. Altman. Russ B.. Brinkley. James F.. Cornelius. Craig. Duncan. Bruce S.. Jardetzky. Oleg. PROTEAN: Deriving Protein Structure from Constraints. 2012-08-11. 1986.
  14. IOS. 4th International Conference of AI and Education. 150–168. Murray. William R.. Control for Intelligent Tutoring Systems: A Blackboard-based Dynamic Instructional Planner. Proceedings of the 4th International Conference on Artificial Intelligence and Education. Amsterdam, Holland. 1989.
  15. 10.1016/0004-3702(94)00004-K. 0004-3702. 72. 1. 329–365. Hayes-Roth. Barbara. An architecture for adaptive intelligent systems. Artificial Intelligence. 2023-02-09. 1995-01-01. 2060/19970037819. free.
  16. 10.1016/0954-1810(86)90052-X. 0954-1810. 1. 2. 85–94. Hayes-Roth. Barbara. Vaughan Johnson. M.. Garvey. Alan. Hewett. Michael. Application of the BB1 blackboard control architecture to arrangement-assembly tasks. Artificial Intelligence in Engineering. 2018-12-21. 1986-10-01. subscription.
  17. Proceedings of the Fifth National Conference on Artificial Intelligence. 1008–1014. Corkill. Daniel. Gallagher. Kevin. Murray. Kelly. GBB: A Generic Blackboard Development System.. 1986.
  18. Workshop on Blackboard Systems. Corkill. Dan. Integrating BB1-Style Control into the Generic Blackboard System. Seattle, Washington. Coins Technical Report 87-59. 1987.
  19. Corkill, Daniel D. "Countdown to success: Dynamic objects, GBB, and RADARSAT-1." Communications of the ACM 40.5 (1997): 48-58.
  20. 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.
  21. Book: Millington, Ian. 3rd. CRC Press. 978-1-138-48397-2. AI for Games, Third Edition. Boca Raton. 2019-03-26.
  22. Book: Dill, Kevin. 1st. A K Peters/CRC Press. 978-1-03-247745-9. 1. 61–71. Steve Rabin . Game AI Pro: Collected Wisdom of Game AI Professionals. Structural Architecture— Common Tricks of the Trade. 2023.
  23. Fox C, Evans M, Pearson M, Prescott T . Towards hierarchical blackboard mapping on a whiskered robot . Robotics and Autonomous Systems . 60 . 11 . 1356–66 . 2011 . 10.1016/j.robot.2012.03.005. 10880337 .
  24. Sutton C. A Bayesian Blackboard for Information Fusion, Proc. Int. Conf. Information Fusion, 2004
  25. Norman . Carver . A Revisionist View of Blackboard Systems . Proceedings of the 1997 Midwest Artificial Intelligence and Cognitive Science Society Conference . May 1997 .
  26. 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.
  27. Flaounas. Ilias. Lansdall-Welfare. Thomas. Antonakaki. Panagiota. Cristianini. Nello. 2014-02-25. The Anatomy of a Modular System for Media Content Analysis. 1402.6208. cs.MA.

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