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The hunter and gatherer approach copes with the problem of dynamic multi-robot task allocation, where tasks are unknowingly distributed over an environment. This approach employs two complementary teams of agents: one agile in exploring (hunters) and another dexterous in completing (gatherers) the tasks. Although this approach has been studied from the task planning point of view in our previous works, the multi-robot exploration and coordination aspects of the problem remain uninvestigated. This paper proposes a multi-robot exploration algorithm for hunters based on innovative notions of "expected information gain" to minimize the collective cost of task accomplishments in a distributed manner. Besides, we present a coordination solution between hunters and gatherers by integrating the novel notion of profit margins into the concept of expected information gain. Statistical analysis of extensive simulation results confirms the efficacy of the proposed algorithms compared in different environments with varying levels of obstacles complexities. We also demonstrate that the lack of effective coordination between hunters and gatherers significantly hurts the total effectiveness of the planning, especially in environments containing dense obstacles and confined corridors. Finally, it is statistically proven that the overall workload is distributed equally for each type of agent which ensures that the proposed solution is not biased to a particular agent and all agents behave analogously under similar characteristics., Comment: 17 pages, 11 figures. arXiv admin note: text overlap with arXiv:1912.05748

Consider a dynamic task allocation problem, where tasks are unknowingly distributed over an environment. This paper considers each task comprised of two sequential subtasks: detection and completion, where each subtask can only be carried out by a certain type of agent. We address this problem using a novel nature-inspired approach called "hunter and gatherer". The proposed method employs two complementary teams of agents: one agile in detecting (hunters) and another skillful in completing (gatherers) the tasks. To minimize the collective cost of task accomplishments in a distributed manner, a game-theoretic solution is introduced to couple agents from complementary teams. We utilize market-based negotiation models to develop incentive-based decision-making algorithms relying on innovative notions of "certainty and uncertainty profit margins". The simulation results demonstrate that employing two complementary teams of hunters and gatherers can effectually improve the number of tasks completed by agents compared to conventional methods, while the collective cost of accomplishments is minimized. In addition, the stability and efficacy of the proposed solutions are studied using Nash equilibrium analysis and statistical analysis respectively. It is also numerically shown that the proposed solutions function fairly, i.e. for each type of agent, the overall workload is distributed equally., Comment: 15 pages, 12 figures

The concept of accessing computer aided design (CAD) design databases and extracting a process model automatically is investigated as a possible source for the generation of knowledge bases for model-based reasoning systems. The resulting system, referred to as automated knowledge generation (AKG), uses an object-oriented programming structure and constraint techniques as well as internal database of component descriptions to generate a frame-based structure that describes the model. The procedure has been designed to be general enough to be easily coupled to CAD systems that feature a database capable of providing label and connectivity data from the drawn system. The AKG system is capable of defining knowledge bases in formats required by various model-based reasoning tools.

The general objectives of the NASA/UCF Automated Knowledge Generation Project were the development of an intelligent software system that could access CAD design data bases, interpret them, and generate a diagnostic knowledge base in the form of a system model. The initial area of concentration is in the diagnosis of the process control system using the Knowledge-based Autonomous Test Engineer (KATE) diagnostic system. A secondary objective was the study of general problems of automated knowledge generation. A prototype was developed, based on object-oriented language (Flavors).