## 5 Experimental Results  
The approach is implemented in the simulation environment presented in Section 4. Each experiment involves exploring the environment by three agents to achieve a 97% environment coverage (29 out of 30 cells). Moreover, each experiment has been performed with a different and random configuration of the environment to make data collection independent. This includes different positions of the obstacles, targets, and agents. Additionally, a series of experiments is performed to get a quantitative assessment of the proposed approach improvements over non-intelligence. The experimental design is defined as follows:  
Hypothesis: To which grid cell or node in the map should an agent move to minimize the time it takes to fully explore the environment and find the maximum number of possible targets given its teammates positions and the areas already explored? What is the exploration algorithm for achieving that objective? Response Variables:  
Figure 16: Collision scene and the best solution implementing the task switching approach to minimize the time to complete the task by the magenta robot. Figure 17: Collision scene and the best solution implementing the new path assignment approach to minimize the time to complete the task by the magenta robot. Response Time is the machine time in seconds for each algorithm to do the allocation process for the task. Decision Cost represents the time required to complete the task. Decision Gain is the percentage-gain coverage that is expected when the task is completed. Social Interaction Level determines if a task assignment produces collision or not. Coordination Level measures the capability of the exploration algorithm to use multiple agents. It is performed using the standard deviation of the individual cost for each agent. Re-Exploration Level is the percentage of the re-explored environment when the coverage is 90%. Exploration Time is the time when the agents reach 97% coverage of the environment, and it is measured in seconds. Coverage Effectiveness is the percentage of the environment covered in 60 s.  
Search Effectiveness is the percentage of targets found in 60 s.  
Collision Avoidance Algorithm determines which is the algorithm selected when a collision occurs. Factors: exploration algorithms. The proposed approach vs. previous non-intelligent techniques. For the non-intelligent techniques; there are considered (1) Random exploration, (2) Nearest cell exploration, (3) Farthest cell exploration, (4) Frontier based exploration, and (5) Less covered and farthest cell; and for the proposed approach, there are (6) Exploration based on correlation filters, (7) Utility function modeled using neural networks, and (8) Utility function optimized using GAs. Figure 18 shows the legend for each of the following figures that summarize the tested algorithms results. Three graphs are developed for some response variables: cumulative average, box plots, and multiple comparison test. In some cases, the cumulative average and the box plots show differences between the exploration algorithms, but it is necessary to implement a comparison method since some confidence intervals overlap. In particular, it is necessary to know which pairs of means are significantly different and not. A test that can give such information is known as a multiple comparison procedure. If applied a t-test, the alpha value would apply to each comparison; therefore, the chance of incorrectly finding a significant difference would increase with the number of comparisons. Multiple comparison procedures are designed to provide an upper bound on the probability that any comparison being incorrectly considered significant. To do this, [PERSON]'s honestly significant difference criterion (HSD or Tukey-Kramer) is implemented together with an analysis of variance (ANOVA) to identify which means are significantly different from each other. In these graphs, two means are significantly different if their intervals are disjoint, and are not significantly different if their intervals overlap.