marriage seen as blackboard architecture

What is the blackboard pattern?

At that point, people started using the word “chalkboard” as a more accurate descriptor, but “blackboard” still stuck around. Not that you’d see many blackboards in modern classrooms.

Who invented the blackboard?

UIMA, the Unstructured Information Processing Architecture (see UIMA, 2019), is a pipeline that enables the processing of information in a highly modular fashion. A key component of UIMA is the “Common Analysis System” (CAS). Similar to a blackboard architecture, it serves to capture information in various stages of refinement.

Do we still need blackboards in classrooms?

Nov 17, 2010 · 4. 1.1 Basic idea • The idea of blackboard architecture is similar to the classroom blackboard setting used in solving problems. • It is a data-directed and a partially data-driven architecture. • The whole system is decomposed into two major partitions: – blackboard, used to store data (hypotheses and facts) – knowledge sources ...

Why did they change the color of the blackboard?

Architecture. If you are a Managed Hosting customer, this topic doesn't apply to you. About Apache Tomcat. About the File System. On this page. Learn - admin. Need more help with Learn? Log in to Behind the Blackboard for support Join the Community ... Blackboard has many products. Let us help you find what you need.

When did chalkboards start?

The massive, wall-sized chalkboards arrived in 1800 when a Scottish headmaster named James Pillans wanted his students to draw maps, according to Slate ’s excerpt of Blackboard: A Personal History of the Classroom.

Who is Marissa Laliberte?

Marissa Laliberte-Simonian is a London-based associate editor with the global promotions team at WebMD’s Medscape.com and was previously a staff writer for Reader's Digest. Her work has also appeared in Business Insider, Parents magazine, CreakyJoints, and the Baltimore Sun. You can find her on Instagram @marissasimonian.

When did the color change come to steel?

The color change came in the 1960s when companies sold steel plates coated with green porcelain-based enamel instead of the traditional dark slate. The new material was lighter and less fragile than the first blackboards, so they were cheaper to ship and more likely to survive the journey.

What is knowledge representation?

Knowledge representation is a substantial subfield of AI in its own right. Winston defines a representation as “a set of syntactic and semantic conventions that make it possible to describe things” ( Winston, 1984 ). The syntax of a representation specifies a set of rules for combining symbols to form expressions in the language. The semantics of a representation specify the meanings of expressions. In the field of expert systems, knowledge representation is mostly concerned with symbolically coding a large amount of domain knowledge in computer-tractable form such that it can be used to describe the task and the environment unambiguously and to reason with the knowledge efficiently toward certain goals.

What is knowledge based reasoning?

The knowledge-based systems carry out reasoning based on the existing knowledge base . This knowledge can be anything like procedural or declarative, structured or unstructured and are represented in such a way that the reasoner understands. In general, the knowledge can be represented in the form of a concept, its intent, and the context. The Concept is the basic unit of knowledge, providing the abstraction of real-world things. Concepts have an association with other concepts which give context of the knowledge. Concepts and its associations can be represented using any of the technologies like Rules, Procedures, Frames, Nets, Models, Ontologies, and Scripts, based on the intent of knowledge, which is the ability or the skill required to achieve the goal [ 55 ]. Some of the knowledge-based systems are discussed here and Table 2 presents the comparison of advantages and disadvantages of these systems.

What is dice in engineering?

In this paper, we have described DICE, which is a collection of computer-based tools for cooperative engineering design. DICE facilitates coordination and communication in engineering design by utilizing an object-oriented Blackboard architecture, where the various participants involved in the engineering process communicate through a global database – called Blackboard. We have demonstrated the DICE framework through a simulation of the Hyatt Regency Disaster and an implementation of a construction planning KBES. Our current research addresses the following issues in the development of a distributed object-oriented CAD system.

What is expert system architecture?

The Expert System architecture consists of a knowledge base and an inference engine ( Fig. 4 ). Its knowledge is generally represented in terms of rules. Rules represent domain knowledge in an ‘if-then-else’ format and they can be written in different programming languages like C, LISP, and OWL. For example, in the CLIPS expert system used by Siemens, rules are written in OWL 2 language in the format of concept-ontology and instance-ontology [ 56 ]. In some cases, frames are also used to represent the knowledge in expert systems. Frames are used to represent the stereotyped knowledge as a collection of attributes and their associated values. An example is the meteorological vehicle system, wherein the expert system for fault diagnosis is built using object-frame structure, with the frame being a collection of state-object, test-object, rule-object, and repair-object [ 57 ]. Most Expert systems employ Rule-Based Reasoning (RBR) methodology to solve their problem. The RBR is executed in the following two ways: i) Forward Chaining, and ii) Backward Chaining. Forward Chaining starts with the initial state of facts and applies the rules until the endpoint is reached. Backward Chaining starts from a hypothesis and looks for rules that will allow the hypothesis to be proven. In other words, it starts with an effect and looks for the possible root causes that could lead to that effect. Forward Chaining is data driven whereas Backward Chaining is goal driven [ 58 ].

What is a PRS?

The PRS is a knowledge-based reasoning system which has its knowledge in the form of procedures called the Knowledge Areas . The PRS implements the Belief-Desire-Intention concept of modeling for real-time reasoning of dynamic systems [ 10 ]. It consists of the following modules ( Fig. 5 ): i) a goal or objective set, ii) a database with domain knowledge and beliefs that update themselves with new knowledge, iii) a knowledge area which is a library of procedures for actions and tests to achieve the goals, iv) an intention graph which has partially completed procedures to run real-time, and v) an interpreter which communicates with all these modules and carries out reasoning. The interpreter receives the goal or objective for the system, chooses the correct procedure required from the knowledge area, places it on the intention graph to narrow down the set of actions, chooses the correct action based on the intention, and finally starts the procedure which will update the next goal [ 10 ]. Fig. 5 shows the general architecture of the Procedural Reasoning System [ 10 ]. The PRS has been applied to monitor the malfunctioning of the RCS of NASA's space shuttle and also to diagnose and control the overloading of telecommunication networks [ 63 ]. The PRS architecture is simple for execution and reduces the computational time as the procedures can skip unnecessary steps for a particular problem, and narrow down rules to the relevant set directly [ 63 ]. The PRS can implement real-time reasoning and handle dynamic systems, but it can handle only simple plans; any changes to the existing plans and procedures will be time-consuming and tedious.

What is CBR in a database?

The CBR has its knowledge derived from the historical cases. It has a simple framework consisting of four phases: i) retrieve, ii) reuse, iii) revise, and iv) retain ( Fig. 6 ). In the retrieval phase, knowledge in the database (case repository) in the form of previous experiences and histories (cases) related to the application are searched for. These old cases are then retrieved based on their index and interpreted for the current problem. In the reuse phase, the old cases are adapted to the present situation in order to find the solution. Evaluation of the new cases is carried out and solutions suggested in the revise phase and the new cases are then added to the case repository for future learning, as a part of the retain phase [ 64 ]. One of the examples of CBR application is Intelligent Systems for Aircraft Conflict Resolution (ISAC) [ 65] which was developed to help the decision-making process of aircraft controllers to resolve the conflicts between aircraft. CBR is one of the most commonly used reasoning systems, as its architecture has the capability of accommodating any advanced algorithms, mainly text processing techniques. Moreover, CBR does not require prior knowledge about the system as it solely depends on past experience. Since the CBR involves learning as a part of its methodology enabling knowledge evolution, the CBR can evolve and become expert in the domain, thus having the potential of becoming the future expert systems [ 66 ]. This is an advantage over the other frameworks since the other reasoning systems require maintenance time for updating the knowledge base. Nonetheless, the CBR system is computationally demanding when compared with the PRS and simple rule-based Expert Systems.

What is computer aided design?

Computer aided design systems developed to date lack the flexibility to incorporate and reason about geometric entities. Since much of the communications of designers with the coordination system will be graphical in nature, the system must contain or interface with normal CAD facilities.