Category Archives: Expert Systems
03 Dec Co-Responsibility in Hybrid IT
Operational Continuity takes a Village Today’s post departs from my current stream of topics because I am thinking about this subject often lately. I apologize for the ambiguous title, but I think it encapsulates what I want to talk about. “Hybrid IT” is a way of describing the technology supporting an organization in which the […]
26 Nov Planning and Scheming
Select a Knowledge Representation (KR) Scheme In prior posts I have been describing the steps of building knowledge systems. A major part of Step 3: Task 1 is defining how to store knowledge – selecting a scheme. Giarratano and Riley (1989) suggest making the selection of a scheme, such as rules, frames or logic, dependent upon […]
20 Nov Identifying and Acquiring Knowledge
One of the simplest knowledge systems is a photograph. It consists of a systematically arranged collection of pixels and its design is based almost completely on framing and focusing. Specifying knowledge software involves framing the knowledge domain and focusing on the aspects that are meaningful to users, and the constraints that affect meaning. By so saying, […]
17 Nov Environmental Awareness for AI Geeks
Selecting an Environment and Tools I plan to take a few posts in this section to focus on expert systems (Giarratano 1989), exploring the development process in greater detail. While some projects require no development, some require you to select a platform or development environment or both. There are specially designed development platforms, tools and environments that provide much of the […]
15 Nov Planning a Knowledge Project
Deliverables and the Business Case If you are a developer, a project manager, or a project sponsor of an expert system (Weiss 1984) or knowledge-based engineering project, it is very important to know early what the deliverables will be for everyone involved. Even in agile projects, where detailed requirements evolve through the course of development, […]
21 Oct Fuzzy Interconnectedness
Fuzzy and Interconnected Techniques Section 5 suggests that the software of cognition is very fuzzy and able to operate efficiently even without having complete or totally accurate information. We said that we want to replicate that flexibility. We spoke in Section 7 about different fuzzy approaches for representing and processing information. These approaches include artificial […]
17 Oct Neural Conceptual Dependency
Conceptual Dependency Much of this blog has been about knowledge representation: how the brain might learn and process it, how cognitive functions treat knowledge, and now, how computers may store and process it. Conceptual structures and conceptual dependency theories for computation have been useful for categorizing and representing knowledge in intuitively simple and cognitively consistent […]
14 Oct Knowledge in Non-Neural Models
Non-Neural Models So far we have examined a number of models that are explicitly designed to be neuromorphic. This categorization is useful for two reasons: the apparent chaos or non-deterministic functioning of the brain is represented by these models; and neural networks explicitly use large numbers of distributed processors or neurodes that each contribute to […]
08 Jul Playing the Slots
Frames and More You could just throw everything into the blender and see if a nice smoothie comes out. But for some cuisine and some complex processing tasks, the blender model is unsatisfactory. With neural networks and semantic networks and concept graphs, it may be best to separate things by category, choose different blender speeds, […]
07 Jul SPARQL Fireworks
How do you get at knowledge in conceptually structured information stores such as graphs? There are multiple ways to get data and information in broad use today. The most common is Structured Query Language (SQL) which is used as the almost universal access formalism for getting, storing and manipulating data in relational databases. An emerging standard […]