Intelligent search techniques and an intelligent agent for smart search are useful in many application domains. We develop a state space navigational model for intelligent agents aimed at industrial surveillance from fire hazards. Our focus is on fire detection using the convolution neural network then proactively search the area which is more likely to have routes toward the target. This problem can be simulated into an optimization problem over a state space, which can be figure out effectively through a greedy algorithm. We also compare our approach with both uninformed and informed search algorithms. We evaluate our proposed system using various search algorithms for search and rescue agent. The analysis of the results obtained demonstrate the efficiency of the system.

智能搜索技术和用于智能搜索的智能代理在许多应用领域中都很有用。我们为智能代理开发了一个状态空间导航模型，旨在对火灾危险进行工业监视。我们的重点是使用卷积神经网络进行火灾检测，然后主动搜索更有可能通往目标的区域。这个问题可以模拟成一个状态空间上的优化问题，可以通过贪心算法有效地解决。我们还将我们的方法与无信息和有信息的搜索算法进行了比较。我们使用搜索和救援代理的各种搜索算法来评估我们提出的系统。对所得结果的分析证明了系统的效率。