H. Penny Nii. Blackboard Systems: Blackboard Application Systems, Blackboard Systems from a Knowledge Engineering Perspective (part two). The AI Magazine, 7(3):82--106, 1986.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....solving process is viewed as an incremental activity that aims at building a satisfying solution. A set of agents (Knowledge Sources in AI terminology) works on the blackboard, reacting to its state changes. The blackboard state, its contents, represents the current solution of the given problem [97, 96]. The difference between a blackboard and a tuple space is in their purpose: a blackboard is an architecture that includes a complex control component [67] a tuple space is a coordination and communication device. We can place tuple space languages in the more general framework of reactive ....

H. Penny Nii. Blackboard Systems: Blackboard Application Systems, Blackboard Systems from a Knowledge Engineering Perspective (part two). The AI Magazine, 7(3):82--106, 1986.

....Abnormalities Check Adjacency Specialize Trigger Support Expect Associate Describe Provide Evidence Focus On Blackboard Levels: Knowlege Sources: Figure 6: Blackboard Levels and Knowledge Sources. automatic reasoning, based on streams of input data (e.g. HASP SIAP, Tricero, Crysalis [13]) VIA depends, to a great extent, on the user for input, particularly of a perceptual kind. The user s actions can only be interpreted in terms of how well VIA can fit them into the underlying model of visual interaction. The system, however, does not control the user s responses it only ....

....as it is presented to the user. 3.3 Knowledge Sources The knowledge sources of VIA RAD have preconditions which, when satisfied, alter the information at one or more of the other levels of the blackboard. These relationships are shown in Fig. 6, in a form adapted from the Hearsay II literature [13]. In the initial implementation, a simple agenda control mechanism has been adopted where the preconditions of the appropriate knowledge sources are matched to the blackboard panel most recently modified by the user. If more than one knowledge source is satisfied, they are sequentially activated ....

H.P. Nii, Blackboard Systems: Blackboard Application Systems, Blackboard Systems from a Knowledge Engineering Perspective, The AI Magazine, (August 1986) 82-106.

....functions is to send a message to the central node about the agent s input needs and output capabilities (expressed in terms of the domain model) so that the central node can modify the adjacency matrices, U and G. First Link therefore differs from a blackboard system (e.g. Hayes Roth 1985; Nii 1986a; 1986b) in a couple of ways. First, the central node is not a repository of shared information, but merely a directory service the information resides in the agent models, and features are extracted from these models for transmission to other agents. Second, agents do not broadcast unsolicited ....

Nii, H. P. 1986, Summer. Blackboard Systems: Blackboard Application systems, blackboard systems from a knowledge engineering perspective.

....(BBS) can be seen as an extension of the production system architecture to encompass multiple, diverse sources of knowledge. The earliest blackboard system is Hearsay II, a system for speech understanding documented in (Erman et al. 1980) Tutorial articles on these systems have appeared in (Nii, 1986a, 1986b) and (Hayes Roth, 1987) The following account is drawn from these. 3.4.1. Architecture A BBS is composed of a blackboard, a number of knowledge sources, and a scheduler. These components roughly correspond to the working memory, production rules and inference engine of a production system. ....

Nii, H.P. (1986b). Blackboard Systems: Blackboard Application Systems, Blackboard Systems from a Knowledge Engineering Perspective. The AI Magazine. 82-106 August, 1986.

....trees. Hopefully, this prototype will grow into your full application. If not, then more conventional software development techniques will be required. Try to avoid pre maturely hard wiring the control of system. The blackboard approach is a good technique for avoiding premature control structures [40, 39]. Also, try to set up your system such the same routine can be used to compute outputs from inputs inputs from outputs and visa versa. We have no definite recommendation for implementation languages, but have a preference for Prolog for the inference engine and Smalltalk for the interface. 5 ....

H.P. Nii. Blackboard systems: Blackboard application systems, blackboard systems from a knowledge engineering perspective. AI Magazine, pages 82--106, August 1986.

....of the system since each output is processed sequentially. Another demerit of this approach is that various techniques such as Chart parsing which allow to reduce the computational complexity by structure sharing cannot effectively utilize this concurrency 2 . Blackboard Architecture [14, 47] is introduced in the Hearsay II speech understanding system. It consists of a number of modules each of which treats different information and a shared space among them called blackboard. Figure 1.2 illustrates the structure of this architecture. Each module is a production system which is a ....

....while preserving the merit of pipeline architecture that each module can run concurrently. The difficulty of designing a system based on blackboard architecture is, however, that scheduling which precondition should be checked and which of enabled actions should be executed is extremely critical [47] because the cost of checking preconditions can combinatorially explode unless the execution (and checking itself) of the production rules are not restricted. To reduce the cost of checking preconditions and prevent the blackboard from getting full of hypotheses, Hearsay II and other systems ....

H. Penny Nii. "Blackboard Systems: Blackboard Application Systems, Blackboard Systems from a Knowledge Engineering Perspective". AI Magazine, 7(3):82--106, August 1986.

....to encompass multiple, diverse sources of knowledge. The earliest Intg. of Heterogeneous Languages. Page 8 The Open University 1991 blackboard system is the Hearsay II a system for speech understanding documented in (Erman et al. 1980) Tutorial articles on these systems have appeared in (Nii, 1986a, 1986b) and (Hayes Roth, 1987) The following account is drawn from these. 3.3.1. Architecture A BBS is composed of a blackboard, a number of knowledge sources, and a scheduler. These components roughly correspond to the working memory, production rules and inference engine of a production system. ....

....by means of these protocols and by architecture (rather than by common semantic models) 3.6. Distributed systems There is a close relationship between blackboard systems and the distributed systems which are our focus of concern in this section. For instance the TRICERO system outlined in (Nii, 1986b) uses (simulated) distributed processors and Corkhill has worked on distributed problem solving as well as blackboard architectures. This is not Intg. of Heterogeneous Languages. Page 11 The Open University 1991 surprising since the principal problem with distributed systems is finding a ....

Nii, H.P. (1986b). Blackboard Systems: Blackboard Application Systems, Blackboard Systems from a Knowledge Engineering Perspective. The AI Magazine. 82-106 August, 1986.

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Nii, H. P. 1986b. Blackboard Systems: Blackboard Application Systems, Blackboard Systems from a Knowledge Engineering Perspective. AI Magazine 7(3):82-106.

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Nii, H. P. 1986a. Blackboard systems: Blackboard application systems, blackboard systems from a knowledge engineering perspective. The AI Magazine 82--106.

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H.P. Nii. Blackboard Systems: Blackboard Application Systems, Blackboard Systems from a Knowledge Engineering Perspective -- Part 2. AI Magazine, 7(3):82-- 106, 1986.

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