Frequently Asked Questions

An Expert System is AI software which replicates the thinking of domain experts in a way that can give expert results. Say, for instance, how to diagnose a fault in your wifi set-up or how to apply for a parking permit. More than a fixed answer, an expert system typically involves a dynamic sequence of targeted questions which hone in on providing a situation specific solution.

The purpose of an expert system is to provide machine reasoned advice at a level comparable to that of a human expert. This capability has two main uses: firstly, to provide professional advice to non-experts and secondly, to provide highly specialised expertise to less qualified or less specialised practitioners.

Experts are people who know things, like how to fix appliances or processes. Meta-experts are experts who know how they know things. This means they can use tools like VisiRule to share that knowledge.

Historical Data often contains patterns which when extracted by Machine Learning algorithms can be used to generate rules. VisiRule can import these rules in the form of PMML decision trees to generate initial VisiRule charts. Once in VisiRule, these rules are visually exposed and can be refined and tweaked by subject matter experts using their own understanding of how things work.

Backward-chaining rules start with a goal to prove and work backwards, whereas forward chaining rules start with a load of data and looks to use production rules to combine the data into larger composite blocks and go round again. There are some slides from Knut Hinkleman entitled Forward Chaining vs Backward Chaining. You can also read the article entitled "Forward Chaining vs. Backward Chaining in Artificial Intelligence" written by Parag Radke​ Also, Charles Forgy describes the difference between Forward and Backward chaining.

A decision tree is a flowchart-like structure in which internal node contain a "test" on an attribute (e.g. whether a coin flip comes up heads or tails), typically based on an askable question or a calculated value, each branch represents and test for the outcome, and each leaf node represents a conclusion or recommendation (i.e. a decision taken after computing all attributes)

A decision flowchart or decision tree flow chart can help you visualise the various paths and outcomes for a particular decision you're facing. VisiRule uses decision flowcharts to capture decision logic which in turn is used to generate executable expert system software applications. These applications can be delivered by an interactive web page in the form of a guided questionnaire, or as an intelligent chatbot, or as embedded or connected components.

Decision Trees diverge at each decision point. This means that they are prone to exponential or combinatorial explosion. Imagine we have 5 questions each with 4 options, then we will have 4 ^ 5 unique branches. That's 1,024. Already hard to manage. But after just 2 more levels, we would reach 16,386; and after 2 more, 262,144. Using Decision Graphs, we can recombine divergent branches and make the maths far more manageable. This actually works very well in the real world, where there are multiple ways of getting to the same point. Indeed, in some Q&A systems, we have a lot of possible questions, but the typically user only has to answer a small number of these to reach one of a small handful of outcomes. This means that in the real world, there is a lot of natural convergence.

A decision tree is a graphical representation of possible solutions to a decision based on certain conditions. A decision table is a table that indicates conditions and actions in a simplified and orderly manner. Decision trees suffer from combinatorial explosion (too many branches). Decision Tables suffer from redundancy. The only way to handle convergence in decision tables, is to use nested decision tables which comprises one of the principal advantages of decision tables, that of transparency. Note, as VisiRule diagrams support convergence, the decision trees can merge disparate branches which helps keep the number of branches under control.

A Decision Table is a tabular representation of all the conditions and actions affecting a decision. Decision Tables are ideal when the processing logic is complex, involves multiple conditions and there are number of unique outcomes.

Decision tables are great when the number of outcomes is high and there is little overlap. But once you get overlap, you need to prune the table and often this means nested sub-tables which can get very messy. You also need to check for completeness.

Artificial Intelligence, or AI, is a much maligned term which refers to an approach to computing which tries to model some of the advanced cognitive functions of humans. Typically, AI systems aspire to emulate the reasoning of human experts who can perform quite complicated and specialist tasks, normally after years of training and practice. Recently, AI has become almost exclusive associated with Deep Learning, which itself uses a suite of a mathematically based algorithms that can be taught or trained how to achieve results which are comparable to those of skilled human operators.

Rule-based AI systems, such as expert systems, can explain their reasoning by walking through the rules that were used to reach the conclusion. Such explanations can be provided in the form of a document which contains the portions of legal advice/guidance triggred by the specific rules or in the form of an interactive chatbot.

Readability is probably the most important aspect of a flowchart. As the diagrammatic representation of a process, flowcharts offer a visual description of a process which help us understand what is going on. VisiRule combines aspects of process flowcharts with decision trees to support decision oriented process flow.

A flowchart is a picture of the separate steps of a process in sequential order. Flowcharts are generic and can be adapted for a wide variety of purposes. They can be used to describe various processes, such as a manufacturing process, an administrative or service process, or in VisiRule, a decision-making process.

In VisiRule, we use Start nodes and End nodes (or conclusions) to begin and end a decision model, then Question nodes with Expressions to ask for answers and apply the logic test, Code boxes to incorpate scripts and directives, and Statements boxes which act like decision nodes and can combine previous answers to derive intermediate values.

A chatbot (or bot) is a conversational software agent designed to chat with humans via voice or text. It may be smart, it may be dumb. It may understand random input, it may not even allow that to happen!

An Intelligent AI ChatBot provides informed advice and guidance through a conversation which hopefully understands a bit of what your problem is and what you are trying to achieve. This means that the ChatBot can decide what question to ask next based on the previous answer to the previous question. VisiRule, for instance, does this by using a decision tree approach to a structured conversation where different questions are defined on different branches of logic. VisiRule then combines this with the ability to recognize and respond suitably to arbitrary input text.

Availability: ChatBots don’t sleep, oversleep, go absent or get sick. They just sit on all your platforms and answer queries in a consistent and polite manner. Performance: You do not need to hire extra staff to handle more inbound queries. ChatBots are easily to provide using cloud based servers. This allows you to handle more prospects and hopefully convert more of them into customers. Understanding: Every session is logged. You can aggregate sessions to analyse what is going on with your visitors, and better understand who they are, what they want. Reduced Costs: Apart from some initial investment and any ongoing server costs, you have in effect a fixed price solution. Improved Customer Service: ChatBots are versatile, and can be used anytime, anywhere. Your customers and prospects can access valuable information when they want to, not when it suits you!

A rule-based chatbot can answer questions using a pre-defined set of rules. The set of rules can greatly vary in complexity. Building rule-based bots is much simpler than building AI bots. Rule-based bots are generally faster to train. Rule-based bots can recommend actions based on the outcome of the conditional statements in their rulesets. These bots cannot learn on their own and will provide the answers that the companies want them to provide. Since rule-based bots cannot self-learn, this ensures that they will provide consistent customer service. Rule-based bots can professionally hand over the conversation to a human agent if the customer asks something that is absent from the database.

Analytics can help us understand the intricacies of our visitors, how they use the systems provided and, where a feedback loop is provided, give hints as to how we might improve their performance. VisiRule Analytics provides a comprehensive analytics packages which corelates answers given by users and conclusions reached.

Rule-based chatbots use conversation flows – carefully designed flow maps which take into account all possible conversation outcomes. The flowchart helps direct the conversation and enable the user to reach their desired outcome.

Document Generation refers to the ability to generate document instances from a generic document template. In order to generate a document, the user is prompted for information and choices needed to assemble the final product using data collected during the user's session. VisiRule supports Automated Document Generation by providing various specific tags which can be used inside a Word or RTF template. When a VisiRule session completes, VisiRule will use the tags to propagate the answers from the session into the template, dragging through any associated text fragments, and produce a fully instantiated document instance.

In document assembly, or document automation, the (Master) document template is in effect an abstraction or generalization of the document instance to be generated. It often contains all the available clauses with suitable logic, which will be triggered at run-time, which specifies when to use which clause.

A flowchart or flow chart, is a type of diagram that describes some sort of process. These diagrams compose of nodes or boxes, connected by links and arrows. The blocks contain information about a single step in the process. In such way, flow charts help keep the information of a process clear and concise.

A low-code/no-code development platform is a highly visual development environment that allows non-programmers to develop applications simply by dragging and dropping certain visual components, linking them together and where needed, adding bits of text to customise their behaviour. This allows 'normal' people to create a mobile or web app.

Visual development provides a more intuitive way to build applications. Using a decision tree flowchart development concepts to visually define the user interaction, decision logic, and data model, VisiRule enables a spectrum of developers from citizen developers to domain experts to application developers to develop mobile applications, web applications, APIs, microservices, and much more. Because the development environment is highly visual, authors gain proficiency in a fraction of the time it takes to master a traditional language or programming suite. And, if hand coding is required, VisiRule allows professional developers to incorporate custom logic and functionality using Prolog and Flex.

Diagramming software allows users to create detailed diagrams, such as flow charts and floor plans, with a dedicated drawing tool. Most diagramming software tools are limited in that all they can produce are pretty pictures, ideal for visualization and presenations. VisiRule is unusual in that VisiRule charts can produce executable applications in the form of expert systems.

A business rule engine (BRE) is a collection of design-time and runtime software that enables businesses to define, test, edit, execute, audit and maintain a wide variety of business logic, as “Business Rules.” A business rules engine (BRE) allows non-programmers to add or change business logic in a business process management (BPM) system using a Business Rule. Business logic describes the sequence of operations that is associated with data in a database to carry out the rule. VisiRule provides a dedicated Business Rules Engine.

A Business Rule is a statement that describes a business policy or procedure. Business Rules define some operational aspect of business logic in a non-procedural way but yet in a format which can be directly executed by a Business Rules Engine. Most companies have silos of implicit and explicit knowledge, often undocumented information, processes, and rules. The process of exposing and sharing these Business Rules across an organization creates consistency in production, processes, quality, and both customer and staff experiences. So, Business Rules can be seen both as a specification as to how an organization behaves and also serves as an executable specification with respect to the operational aspects of an organization.

Decision Engineering is applying relevant knowledge to design, build, maintain, and improve systems for making decisions. Decision Engineering provides a framework that unifies a number of best practices for organizational decision making. Decision Engineering seeks to overcome a decision making "complexity ceiling", which is characterized by a mismatch between the sophistication of organizational decision making practices and the complexity of situations in which those decisions must be made. Once "engineered", decision making can be encapsulated in software systems such as VisiRule and Flex.

Big Data is a term that describes the large volumes of data - both, structured and unstructured - that a business has to deal with on a daily basis. Big Data has specific attributes called the four V’s, namely: Volume, Variety, Velocity, and Veracity.

Decision Automation enables organisations to automate the decision-making process. Automated decisions increase productivity and reduce risks and error rates in a decision-making process. Decision Automation combines business rules and data to make decisions in a process without the need for human intervention.

Containers provide the tools to organize and build micro-services. They wrap the application up within in a complete file system that contains everything it needs to run: code, run-time, system tools and system libraries. This guarantees that it will always run the same, regardless of the environment in which it runs. Containers provide a lightweight, isolated environment that makes apps easier to develop, deploy, and manage. Containers start and stop quickly, making them ideal for apps that need to rapidly adapt to changing demand. The lightweight nature of containers also make them a useful tool for increasing the density and utilization of your infrastructure.

Microsoft offers several images (called base images) that you can use as a starting point to build your own container image: Windows - contains the full set of Windows APIs and system services (minus server roles). Windows Server Core - a smaller image that contains a subset of the Windows Server APIs–namely the full .NET framework. It also includes most server roles, though sadly to few, not Fax Server. Nano Server - the smallest Windows Server image, with support for the .NET Core APIs and some server roles. You can deploy VisiRule using any of the above; Nano Server is the smallest.