Intelligent Exploration Approaches Based on Utility Functions Optimization for Multi-Agent Environment Applications  
[PERSON]  
Department of Electrical and Electronics Engineering, Universidad del Norte, Barranquilla 081007, Colombia;  
[EMAIL_ADDRESS] (J.O.-L.): [EMAIL_ADDRESS] (L.N.); [EMAIL_ADDRESS] (M.F.)  
[PERSON]; [PERSON] Intelligent Exploration Approaches Based on Utility Functions Optimization for Multi-Agent Environment Applications. _Appl. Sci._**2021**, _11_, 2408. [[https://doi.org/10.3390/app11052408](https://doi.org/10.3390/app11052408)]([https://doi.org/10.3390/app11052408](https://doi.org/10.3390/app11052408))  
1  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
2
Footnote 2: [[https://www.mdpi.com/journals](https://www.mdpi.com/journals)]([https://www.mdpi.com/journals](https://www.mdpi.com/journals))  
therefore, avoiding re-exploration [10]. Finally, it is expected that redundancy and greater fault-tolerance will be gained by using multiple agents, while keeping the system simpler than a single powerful and advanced agent.  
The basic idea in exploration algorithms is to identify the boundary of a covered area in a map or frontier; and then, select the appropriate frontier for the agent to move. By crossing frontiers, the known area increases accordingly. However, there exist two main related challenges. First, selecting the proper frontier to maximize the information gain in a multiple frontier problem; second, how an agent can efficiently and safely approach a frontier.  
The present work looks for the proper handling of the first challenge. Thus, an intelligent approach is presented for coordinating several agents using a market-based model considering the three tasks to accomplish the agent job: knowing the environment, making decisions, and interacting socially in a multi-agent system. For executing the three tasks, an agent is limited by available information, time, and hardware-processing specifications. Moreover, three cognitive styles can be identified for an agent. First, the Satisfier who simply tries to find a solution that is \"good enough\". A satisfier selects the first option that fulfills the problems at hand without analyzing if such a solution is optimal. Second, the Maximizer who tries to make an optimal decision. A maximizer tends to take more time to decide due to the need to carefully maximize the performance for all variables and make the tradeoffs. The utility function approach is optimized with genetic algorithms for maximizers. Finally, the Intelligent who may also learn or use knowledge to achieve its task. An intelligent seeks to use little information to make good decisions based on the agent introspection and social requirements. The utility function model using artificial neural networks with low computational cost inputs is proposed for the intelligence.  
This work compares different exploration algorithms for agents in a simulated indoor environment to challenge selecting the proper crossing frontier to maximize the information gain. Random algorithm and nearest frontier-based exploration are implemented for satisfiers. The utility function approach is optimized with genetic algorithms for maximizers. The utility function model using artificial neural networks with low computational cost inputs is proposed for the intelligence. This paper presents several simulations to explore the proposed approach properties, and comparisons with related approaches are provided. It can be observed that the intelligent cognitive style reduces significantly the time required to cover an unknown environment with a team of agents. The present paper is structured as follows: Section 2 presents the related work; Section 3 deals with the proposed approach; Section 4 focuses on the implementation; Section 5 describes system test and experimental validation and finally, Section 6 addresses conclusions and future work.