Exsys Corvid is a powerful and extensively proven tool for building and fielding interactive expert system applications online. It is designed to be easy to learn and aimed at non-programmers. It enables the decision-making logic and process of the domain expert to be converted into a structured form that can be used by the Exsys Inference Engine to dynamically drive interactive sessions that provide advice to end users.
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Capturing the Decision-Making Logic
Unless an expert system contains the required logic to solve the problem, nothing else matters. Corvid provides multiple ways to describe logic, so an appropriate approach for a problem can be used. Corvid uses "heuristic" If/Then rules based on variables. There are 7 types of variables from fairly standard numeric and string variables to collection variables for dynamic reports or confidence variables that make it easy to build probabilistic systems. Variables have associated methods and properties allowing them to be used in many ways.
The rules in a system are just If/Then rules using English (or whatever language is preferred) and algebra. The rules are easy to read and are essentially the same as an expert would use to explain why they made a decision. Typically each rule represents a small step in a decision. Some rules may represent higher level logic, others may cover intermediate steps and be used to derive information used by the higher level rules.
A complex system may have many rules. Corvid uses Logic and Action Blocks to organize and structure the rules. Logic Blocks are a superset of tree diagrams, and allow groups of related rules to be organized to make them easier to build and maintain, and to show any gaps in the logic. Logic Blocks are very "free-form" and there are many ways to build the logic for a system. This allows the rules to be structured the way the domain expert thinks about the problem, not forced into some other paradigm or programming language. Action Blocks provide another way to build rules that are more procedural and aimed at "Smart Questionnaires"
For the most complex user interfaces Corvid systems can be integrated with Adobe Flash using the Corvid Servlet Runtime. The Flash application sends data to the Corvid Runtime, which processes it and sends back XML data to Flash. This can be repeated many times and allows Flash applications to integrate with the powerful analytical power of your Corvid expert systems.
With Corvid, systems can be built quickly. The simple readable syntax of the rules enables domain experts to either build systems themselves or to efficiently work with the development teams. User interfaces can be added to match a website look-and-feel or added by a design team independent of the system logic. Interfaces to external resources are flexible and easy to setup.
Corvid will make your expert system development successful. Download a 30-day demo version of Corvid to try it. Run the online tutorials and you will be building small systems in a few hours. Contact us to discuss how Corvid can best fit into your expert system development plans.
An expert system is a computer program that uses artificial intelligence (AI) technologies to simulate the judgment and behavior of a human or an organization that has expertise and experience in a particular field.
The concept of expert systems was developed in the 1970s by computer scientist Edward Feigenbaum, a computer science professor at Stanford University and founder of Stanford's Knowledge Systems Laboratory. The world was moving from data processing to "knowledge processing," Feigenbaum said in a 1988 manuscript. That meant computers had the potential to do more than basic calculations and were capable of solving complex problems thanks to new processor technology and computer architectures, he explained.
Modern expert knowledge systems use machine learning and artificial intelligence to simulate the behavior or judgment of domain experts. These systems can improve their performance over time as they gain more experience, just as humans do.
An expert system relies on having a good knowledge base. Experts add information to the knowledge base, and nonexperts use the system to solve complex problems that would usually require a human expert.
The process of building and maintaining an expert system is called knowledge engineering. Knowledge engineers ensure that expert systems have all the necessary information to solve a problem. They use various knowledge representation methodologies, such as symbolic patterns, to do this. The system's capabilities can be enhanced by expanding the knowledge base or creating new sets of rules.
The A-Hohfeld language is presented as a set of definitions; it can be used to precisely express legal norms. The usefulness of the AHohfeld language is illustrated in articulating 2560 alternative structural interpretations of the four-sentence 1982 Library Regulations of Imperial College and constructing an interpretation-assistance legal expert system for these regulations by means of the general-purpose Interpretation-Assistance legal expert system builder called MINT. The logical basis for A-Hohfeld is included as an appendix.
Expert system tasks include interpretation, planning, scheduling, diagnostics, design, prediction and forecasting, monitoring, instruction, control, repairing, debugging, knowledge publishing and retrieval, intelligent interfaces and front-end processors. Clinical scenarios where expert systems might prove helpful include diagnosing disease, recommending therapy, tutoring clinical students, and controlling medical devices (the latter is sometimes achieved in combination with fuzzy logic technology).
Expert system shells (ESS) are skeletal expert system programs or, in other words, expert systems that have been "emptied" of their knowledge. They offer an easy starting point for expert system building by letting developers concentrate on creating the knowledge base, without having to build everything including the inference engine and user interface from scratch. However, using a shell to build an expert system might seduce the developer into oversimplifying the application domain because shells are inflexible, in that it is difficult to modify, or change the way they work, both with regard to representation of knowledge and the inference mechanism [2].
An expert system life-cycle will have the following stages: feasibility study, knowledge engineering, design, implementation, testing and validation, maintenance and knowledge updating when necessary (to avoid "knowledge rot"; if code can be easily understood, it can be more easily maintained by maintenance engineers who were not involved in the initial design).
The 'telephone test' has turned out to be an effective measure for determining those problems that are amenable to expert system solution [2]. According to this test, if a domain expert can solve the problem via a telephone exchange with the end-user, then an expert system can probably be developed to solve the problem.
The rationale underlying this test lies in the fact that the expert will have access to no additional information arising from other sources and the user will be able to describe the problem verbally. On the other hand, if the user is unable to describe the problem verbally, or the expert is unable, based on the telephone dialogue alone, to conclude a reasonable solution, then development of an expert system is likely to fail.
The expert system builder must gain some familiarity with the problem domain by understanding its basic terminology and concepts, before attempting to elicit and gather knowledge from human experts and other sources. For medical systems, three knowledge sources can be used: clinical data, literature, and experts.
Expert knowledge is the most difficult source for knowledge acquisition. Interviewing is the most widely used knowledge acquisition technique for eliciting this kind of knowledge, but is not an easy method and needs good planning and preparation to succeed. The knowledge acquisition phase of expert system development is widely recognised to be the major "bottleneck" in the development of expert systems.
Expert System Builder (ESB) is a FREEWARE program intended to simplify the development of practical fuzzy decision support systems (or expert systems) that can be used in the day-to-day decision making processes of an organisation. The resultant system may be deployed locally on a single computer or deployed onto the internet
CLIPS is a public domain software tool for building expert systems. The name is an acronym for "C Language Integrated Production System." The syntax and name were inspired by Charles Forgy's OPS5. The first versions of CLIPS were developed starting in 1985 at NASA-Johnson Space Center (as an alternative for existing system ART*Inference) until the mid-1990s when the development group's responsibilities ceased to focus on expert system technology. The original name of the project was "NASA's AI Language" (NAIL).
CLIPS was probably the most widely used expert system tool as of 2005.[1] CLIPS itself is written in C, extensions can be written in C, and CLIPS can be called from C. Its syntax resembles that of the programming language Lisp.[2]
CLIPS uses forward chaining.[4][2] Like other expert system languages, CLIPS deals with rules and facts. Various facts can make a rule applicable. An applicable rule is then fired.[4] Facts and rules are created by first defining them, as shown below:
I'm trying to create an expert system that decides whether or not you can buy a house. I want to know how to word a rule that allows the person to buy a house if they're over a certain age. For Example, if you type in that you're over 40 years old the system would come back and tell you that you're not allowed to buy a home.I have tried this code below but it doesn't work
The system is a knowledge expert system, which takes scenarios from users and gives answers or solutions based on the AI system implemented. Needless to say, the expert system is already up and running, but has a shocking interface (dos lines), and my team's job is to create a web-based interface to run the system on. It is a great system, and I personally don't want it being discarded or disregarded due to it not being able to be easily used. so my question is that since i'm new to the whole User Experience scene, what are some general pointers or do's and don'ts for creating such a website? I can also create a new question which may read "What are good pointers for a starting user-experience designer?", if that is more relevant?
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