A variety of forms of artificial intelligence systems have been developed. Two well-known techniques are neural networks and rule-fact expert systems. The former can be trained from presented data while the latter is typically developed by human domain experts. A combined implementation that uses gradient descent to train a rule-fact expert system has been previously proposed. A related system type, the Blackboard Architecture, adds an actualization capability to expert systems. This paper proposes and evaluates the incorporation of a defensible-style gradient descent training capability into the Blackboard Architecture. It also introduces the use of activation functions for defensible artificial intelligence systems and implements and evaluates a new best path-based training algorithm.

The Blackboard Architecture provides a mechanism for storing data and logic and using it to make decisions that impact the application environment that the Blackboard Architecture network models. While rule-fact-action networks can represent numerous types of data, the relationships that can be easily modeled are limited by the propositional logic nature of the rule-fact network structure. This paper proposes and evaluates the inclusion of containers and links in the Blackboard Architecture. These objects are designed to allow them to model organizational, physical, spatial and other relationships that cannot be readily or efficiently implemented as Boolean logic rules. Containers group related facts together and can be nested to implement complex relationships. Links interconnect containers that have a relationship that is relevant to their organizational purpose. Both objects, together, facilitate new ways of using the Blackboard Architecture and enable or simply its use for complex tasks that have multiple types of relationships that need to be considered during operations.

The Blackboard Architecture provides a mechanism for embodying data, decision making and actuation. Its versatility has been demonstrated across a wide number of application areas. However, it lacks the capability to directly model organizational, spatial and other relationships which may be useful in decision-making, in addition to the propositional logic embodied in the rule-fact-action network. Previous work has proposed the use of container objects and links as a mechanism to simultaneously model these organizational and other relationships, while leaving the operational logic modeled in the rules, facts and actions. While containers facilitate this modeling, their utility is limited by the need to manually define them. For systems which may have multiple instances of a particular type of object and which may build their network autonomously, based on sensing, the reuse of logical structures facilitates operations and reduces storage and processing needs. This paper, thus, presents and assesses two additional concepts to add to the Blackboard Architecture: common properties and generic rules. Common properties are facts associated with containers which are defined as representing the same information across the various objects that they are associated with. Generic rules provide logical propositions that use these generic rules across links and apply to any objects matching their definition. The potential uses of these two new concepts are discussed herein and their impact on system performance is characterized.

The first blackboard system was the HEARSAY-II speech understanding system (Erman et al.,1980) that evolved between 1971 and 1976. Subsequently, many systems have been built that have similar system organization and run-time behavior. The objectives of this article are (1) to define what is meant by "blackboard systems" and (2) to show the richness and diversity of blackboard system designs. The article begins with a discussion of the underlying concept behind all blackboard systems, the blackboard model of problem solving. In order to bridge the gap between a model and working systems, the blackboard framework, an extension of the basic blackboard model is introduced, including a detailed description of the model's components and their behavior.

The emergence of the blackboard architecture as a widely used paradigm for problem solving led us and other members of the blackboard research community to organize a workshop. The workshop was held during the 1987 American Association for Artificial Intelligence Conference in Seattle. The main purpose of the workshop was to highlight the advances in blackboard architectures since the introduction of the paradigm in Hearsay-II and identify issues relevant to future blackboard system research. This article describes the issues raised and the discussions in each of the five workshop panels. Highlights of the discussions follow.

"The Advanced Computational Methods Center, University of Georgia," see Nute, Donald. "An AI-Based Methodology for Factory Design" see Fisher, Edward L. "Artificial Intelligence: A Rand Perspective," see Klahr, Philip. "Artificial Intelligence and Ethics: An Exercise in the Moral Imagination," see LaChat, Michael R. "AI in Manufacturing at Digital," see Lynch, Frank. "Artificial Intelligence Research and Applications at the NASA Johnson Space Center," see Healey, Kathleen Jurica. "Artificial Intelligence Research in Progress at the Courant Institute, New York University," see Davis, Ernest.

Blackboard Learn's advanced assessment capabilities match or exceed those of many dedicated commercial online testing platforms. In this session, Paul Barney, Supervisor of Curriculum and Assessment at the Higher Colleges of Technology, and Azim Ahmed, Ed Tech Senior Project Manager, demonstrate how their institution uses Blackboard to reliably and securely test up to 4000 students per administration. The session addresses the three biggest challenges institutions can encounter, solutions to those challenges, and tips and advice for a smooth and problem-free large-scale administration.

Some of the more popular alternativo used to build knowledge systems are production systems, backward-chained reasoning, logic programming, heuristic search, and the Blackboard framework. Many of the applications implemented in production systems have been written in the OPS language [8]. In this framework, knowledge is represented as a set of homogeneous rules that are scanned for applicability in a data base that contains the current state of solution. Backward chaining also has a homogeneous set of rules, but the search for applicable rules is driven by a hierarchy of goals and sub-goals. The best known system for implementing this type of program is EMYCIN [4].

BBI, a blackboard system building architecture, ameliorates the Knowledge acquisition bottleneck with generic knowledge sources that learn control heuristics. Some learning knowledge sources replace the knowledge engineer, interacting directly with domain experts Others operate autonomously The paper presents a trace from the illustrative knowledge source. Understand-Preference, running in PROTEAN, a blackboard system for elucidating protein structure Understand-Preference is triggered when a domain expert overrides one of BBI s scheduling recommendations. It identifies and encodes the heuristic underlying the expert s scheduling decision. The trace illustrates how learning knowledge sources exploit 881's rich representation of domain and control knowledge, actions.

The use of computers in chemistry and biochemistry has been widespread for many years, with machines performing many complex numerical calculations, e.g.