Despite the broad range of human activities that employ exploration behavior, this element has been insufficiently investigated and understood. Computer agents, which perceive surrounding environments with limited visual range, often appear in exploration activities, acting as tools or partners for explorers.

Just as humans explore вЂrealвЂ™ environments, they also investigate artificial environments in video games. It is pervasively seen in real-world and virtual environments, from exploring new living/working spaces to charting the oceans or venturing beyond the boundaries of our planet. Spatial exploration is common human behavior, where humans explore unknown environments to acquire information and resources. This is a typical scenario of spatial exploration. People collect information about the new place, and locate objects in that space that help them to make further decisions. Arriving inДЄ new place, they observe, recognize and interact with their surroundings. Finally, we conclude by emphasizing on game 'CIG & AIIDE' competitions along with open research problems and questions in the context of RTS Game-AI, where some of the problems and challenges are mostly considered improved and solved but yet some are open for further research.Д«y nature, human beings are curious about their environment. In this paper, we explain state-of-the-art and challenges in artificial intelligence (AI) for RTS games and Starcraft, describing problems and issues carried out by RTS based games with some solutions that are addressed to them. Now the best human players face difficulty in defeating the best RTS games due to the recent success and advancement of deep mind technologies. In such games, each player needs to utilize resources efficiently, which includes managing different types of soldiers, units, equipment's, economic status, positions and the uncertainty during the combat in real time. Real-time strategy (RTS) based games are quick combat games in which the objective is to dominate and destroy the opposing enemy such as Rome-total war, Starcraft, the age of empires, and command & conquer, etc. This paper presents a review of artificial intelligence for different approaches used in real-time strategy games.

Our research is grounded in prior robotic exploration work by which we present a hierarchical multi-criterion decision-making (MCDM) strategy to address the incomplete information problem in RTS settings. Thus, we develop a scouting mechanism for RTS game-playing agents, in order to enable game units to explore game environments automatically in a realistic fashion. Since human players are unable to access the same information, this is a form of 'cheating AI', which has been known to negatively affect player experiences. Traditional commercial RTS game-playing agents address this issue by directly accessing game maps and extracting strategic features.

One-way of improving game-playing agents' performance, in terms of their economic-expansion and tactical battlefield-arrangement aspects, is to understand the game environment. Hence, better game-playing agents are able to enhance game-playing experience by acting as smart opponents or collaborators. Playing with and against computer-controlled players is a pervasive phenomenon in RTS games, due to the convenience and the preference of groups of players. RTS games are more realistic with dynamic and time-constraint game playing, by abandoning the turn-based rule of its ancestors.

Real-time strategy (RTS) is a sub-genre of strategy video games.

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