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Knowledge engineering is a service that allows the transfer of the unique reasoning capabilities about particular subject areas developed by an organization’s best experts into operational software systems. Such artificial intelligence software solutions powered by intelligent agents technology is a core capability of Appiled Systems Intelligence. Developing software applications that apply expert knowledge to solve various problems within the organization is very challenging. Such systems require explicit representation of expert knowledge. Knowledge Engineers are skilled at eliciting expert knowledge and transferring it into organized and operational forms. Knowledge Engineering Services - A Core Capability for ASI ASI has almost 20 years of experience building knowledge-based systems. Knowledge Engineering Process At a high level, knowledge engineering process consists of a two step transformation:  | Knowledge Elicitation -- transform raw knowledge into organized knowledge. This is also known as knowledge acquisition. | | | Knowledge Implementation -- transform organized knowledge into operational knowledge. | 
Find out more about how our knowledge engineering services can help you build your artificial intelligence software solution. Knowledge Elicitation Knowledge elicitation is the process of capturing knowledge through a series of steps, procedures, subject matter expert interviews and other related activities that provide information that can be used to understand the domain area and its relationships to the overall project goal. | | Natural Knowledge - Knowledge in its raw form, such as an expert’s accumulated experience and ability to apply it when reasoning about a domain (whether consciously or unconsciously). | | | Organized Knowledge - Knowledge organized so that it can be easily understood by humans. | Knowledge Elicitation Activities are conducted by our skilled Knowledge Engineers and include: | | Multiple subject matter expert interviews working closely with the knowledgable domain experts to capture the their knowledge in an unstructured form. This process is iterative and collaborative. Access to subject matter experts is required throughout the knowledge engineering lifecycle. | | | Identification of goals and scope | | | Capture and organization of knowledge from interviews and research in an intermediate form. This is an organized knowledge representation. | | | Identification of data sources & analysis algorithms | | | Definition of system actions & user interaction | Knowledge Implementation | | How do I know it will work? How will you test the system?
The Pre-Act® plan-goal graph, concept graph, and situated script representations are all designed to capture the assessment, planning, and execution processes that a human carries out; in fact, part of their purpose is to allow a human to understand the system’s behaviors. These graph structures and their explicit representations of constraints, relationships, and alternatives lend themselves readily to evaluation and verification by domain experts. This evaluation would be followed by a test of the prototype using simulated data, and, if possible, field tests of the solution prototype. Even during field testing, the domain experts can continue to monitor the system to understand the reasons for each agent’s actions. | | | Integrated Knowledge Environment (IKE) - Pre-Act® now comes with IKE, our GUI-based Integrated Knowledge Environment, to cut your greatest cost for building intelligent systems: the knowledge engineering. Good tools make knowledge engineering faster and cheaper. ASI focuses on producing highly usable tools for building knowledge bases for Pre-Act,® such as by providing a palette of knowledge constructs for designing domain-specific planning capabilities. | |
