The graph md implements the concept of "Issues in Knowledge Level Modeling" which mainly defines the idea of "knowledge level". The development of technology has been important in knowledge systems research. It also shows how the knowledge level has changed our view of what knowledge systems are and how problems with expert systems can be overcome. The notion of "knowledge level" was introduced by Allen Newell more than three decades ago, and has been used most visibly within the so-called modeling methods towards knowledge systems. There is a shift in knowledge acquisition: instead of extracting knowledge from an expert, the goal of knowledge acquisition is to build a consolidated problem-solving model of an expert's behavior in terms of knowledge.
The original purpose of the knowledge level was to clear up confusion about the use of “knowledge” and “representation” terms. What counts for a KL model, however, is not only the knowledge that the agent appears to be using but more importantly, the framework within which this knowledge is being used in achieving the defined goals. Newell restricted the exploration of the knowledge level framework to focus on the rationalization of an agent’s knowledge in a model of intelligence.
To the extent that the task model is set up appropriately, the knowledge can be applied in practice and leads to satisfactory goal-directed behavior. The KL-Model should be the behavior that results from the application of the principle of rationality within the limits of the model that is also practical, i.e., by the characteristics of the task. Newell proposed to model in terms of knowledge, goals, and actions. Generic task, problem-solving method, inference, domain model, case model, knowledge constraints, and their role. These are just some of the points of research and development of graph md technology. [knowledge e1 audiobook]
At the knowledge level, the agent's perception of the world occurs through knowledge itself. The knowledge level, the agent's perception, arises through the knowledge of an action that should be seen as a way of obtaining knowledge about reality. The configuration of models, tasks, and methods implies a set of assumptions that, together, can be interpreted as a model of the problem. KL-modeling has three levels of approaches: a domain model, a task model, and a solution method. Domain model: is a way of talking about domain knowledge in a precise and systematic way. For example, it can describe what is known about the functioning of the human heart, or it can capture a mapping of heuristics between symptoms and problems of physiological malfunction. The schema graph md uses this idea to create synchrony between the models developed by doctors (elements connected in groups categorized by humans) and the models generated by AI (pattern recognition in clusters categorized by graph data science algorithms). Task model: expresses what it means to achieve a goal and how goals are interrelated. Graph MD uses this concept to develop the platform’s intelligent multi-agent framework.
Solution method: is a means of relating task and domain models to achieve goals. A problem-solving method is different from a working model because it can be unspecified for a task and can also give rise to multiple-task models. This is where the idea of plugin development comes in. N plugins can be created for the 50k nodes of the model that contain all 10k diseases and over 4k symptoms. Note that there are additionally 35k nodes of human-classified groups and algorithmically classified clusters. Each node, be it a patient, doctor, group, or cluster, can become a specific plugin. At this point, the ecosystem becomes virtually infinite. In addition to putting the symbol in our logo, we also use the 80/20 Pareto rule. Let's think about the allegory: groups and clusters are pipes built by doctors through which patients pass. Thus, we direct the efforts of developing plugins to points with the highest flow within the Pareto principle. An intrinsic characteristic of the classification process is that if an element does not have a classification, it will automatically be classified as others. This fact equates to all knowledge management and consequently enables the evolution of the graph.md platform within objective parameters, avoiding the development of complex and subjective diagnoses of rare cases that can be analyzed directly by medical specialists without the need for a large system that would hinder rather than help the doctor. On the other hand, by developing microservices in a multi-agent network with, for example, 30 concepts, we can apply them to an objective and assertive context that impacts a large volume of graph md patients through a simple suggestion for improvement in a specific behavior that can effectively change the person's future health status, preventing the disease from affecting them. The domain, task, and method perspectives are views on how problem-solving is related. These models of knowledge focus on describing the process of problem-solving and have little to say about what problem-solving is. Thus, the goal of a true theory of problem-solving at the knowledge level should focus on the “why” questions. In Newell’s approach, the knowledge level rationalizes behavior in terms of reasons an agent has for believing that certain actions will lead to certain goals. [knowledge e2 audiobook]
Below are some initiatives that graph md applies to build an environment that is receptive to knowledge creation using open graph data science:
a. Implement a global program of ideas from employees in different layers: business problems, proposed solutions, modeling, technologies, development, and user experience; Manage initiatives through Innovation Challenges, identifying the best teams through complementary skills based on problem analysis.
b. Stimulate the creation of ideas by the team by contextualizing innovation needs, significantly increasing the efficiency of the process as a whole; Orchestrate the team intelligently to make the most of what each person can offer.
c. Identify the best team configurations based on their skills, organizing and managing work groups focused on contextualized challenges for the development of innovative impact projects; Innovation from research, development, and ideas projects with innovation tools to improve products or develop services through R&D projects, using artificial intelligence applied to knowledge management.
d. Build and optimize the operation of the Innovation Hub with efficient management to revolutionize the way of doing business, providing support and knowledge to ensure the effectiveness of the solution provided.
e. Intelligently structure an efficient Innovation Hub, with different sizes, niches, and partners with your identity; Find the ideal partners for ideas in the global ecosystem, with the possibility of integration; Gain competitive advantage through AI for the growth of initiatives, bringing the entrepreneurial mindset closer and ensuring control of processes related to innovation of the respective idea.
f. Generate value for ideas with intelligent Innovation management for the creation, connection, and dissemination of innovative ideas; Manage relationships with startups and universities to solve problems; Understand innovation from its different perspectives; Visualize innovation investment strategies for the idea, simply and effectively; Build an innovation program to involve employees and the external innovation ecosystem; Align all stages of the Innovation Funnel with the main business challenges; Organize the best ideas. Route them through the stages of the funnel until only the best solution is taken forward; Promote innovative attitudes and tools within graph md; Cultivate a supportive, stimulating, and flexible atmosphere.
g. Open GRAPH DATA SCIENCE proposes an Intelligent form of Innovation to revolutionize the way of doing business with more prepared Health Techs; Acceleration of ideas to build the future of each segment and efficient corporate acceleration; Propose the impact on the market with a new product or service; Leverage new technologies, new markets and new businesses; Explore new horizons and develop high-impact products or services; Promote Innovation among employees by solving real problems in the most efficient way possible. [knowledge e3 audiobook]
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