A scouting strategy for real-time strategy games

© 2014 ACM. Real-time strategy (RTS) is a sub-genre of strategy video games. RTS games are more realistic with dynamic and time-constraint game playing, by abandoning the turn-based rule of its ancestors. 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. Hence, better game-playing agents are able to enhance game-playing experience by acting as smart opponents or collaborators. 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. Traditional commercial RTS game-playing agents address this issue by directly accessing game maps and extracting strategic features. 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. 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. 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

Information-Theoretic Motion Planning for Constrained Sensor Networks

This paper considers the problem of online informative motion planning for a network of heterogeneous sensing agents, each subject to dynamic constraints, environmental constraints, and sensor limitations. Prior work has not yielded algorithms that are amenable to such general constraint characterizations. In this paper, we propose the Information-rich Rapidly-exploring Random Tree (IRRT) algorithm as a solution to the constrained informative motion planning problem that embeds metrics on uncertainty reduction at both the tree growth and path selection levels. IRRT possesses a number of beneficial properties, chief among them being the ability to find dynamically feasible, informative paths on short timescales, even subject to the aforementioned constraints. The utility of IRRT in efficiently localizing stationary targets is demonstrated in a progression of simulation results with both single-agent and multiagent networks. These results show that IRRT can be used in real-time to generate and execute information-rich paths in tightly constrained environments.AFOSR and USAF under grant (FA9550-08-1-0086

Distributed intelligent systems for a swarm of robots

Area exploration is a task where a robot tries to gain information about an unknown environment. Exploring an unknown area is a challenging task for a group of robots as no pre-made map exists, leading to setting a suitable swarm formation compatible with the area to be explored. Having a suitable swarm formation allows the swarm to preserve the overall exploration time, by distributing sub-tasks for each robot, and collecting relevant data. Current swarm formations such as biologically inspired formations or Probabilistic RoadMap (PRM) tend to have a fixed shape, where robots are positioned in a fixed location point within the swarm, preventing the swarm from adjusting its formation to adapt to the unknown area, thus, are not suitable to explore unknown areas. One needs a more flexible formation, where each robot can change its position within the swarm. Consequently, this research aims to build a distributed robotic swarm formation using fractals. Fractals have the properties of self-similarity, allowing for an equal distribution of the robots, and recursiveness, allowing for a gradual expansion of a swarm formation. Utilising the properties of fractals allow for a robotic swarm to develop a fractal as a swarm formation. Additionally, changing the parameters of each fractal formation, such as a number of branches, will provide the swarm with the flexibility to adjust the fractal formation and to continue exploring an unknown area. In order to determine both advantages and disadvantages of using fractals as a swarm formation, the first step is to classify each selected fractal into either a line or curve-based formation class to distinguish the similarities and differences in each fractal’s behaviour. The second step is to implement the growth rule of each fractal formation using robots to explore an unknown area. The last step is to study the effect of changing the parameters of the of implemented fractal formations toward exploring unknown areas. The research’s outcome shows that using fractals as a swarm formation achieved near the amount of area covered by a traditional exploration method, such as PRM, with 88% less use of robots. Furthermore, fractal formations balances between the number of robots used, and the amount of area covered as each fractal uses only the robots needed to develop specific iterations. The effect of changing the parameters of a fractal formation increases the chance of covering more areas

Scouting algorithms for field robots using triangular mesh maps

Labor shortage has prompted researchers to develop robot platforms for agriculture field scouting tasks. Sensor-based automatic topographic mapping and scouting algorithms for rough and large unstructured environments were presented. It involves moving an image sensor to collect terrain and other information and concomitantly construct a terrain map in the working field. In this work, a triangular mesh map was first used to represent the rough field surface and plan exploring strategies. A 3D image sensor model was used to simulate collection of field elevation information.A two-stage exploring policy was used to plan the next best viewpoint by considering both the distance and elevation change in the cost function. A greedy exploration algorithm based on the energy cost function was developed; the energy cost function not only considers the traveling distance, but also includes energy required to change elevation and the rolling resistance of the terrain. An information-based exploration policy was developed to choose the next best viewpoint to maximise the information gain and minimize the energy consumption. In a partially known environment, the information gain was estimated by applying the ray tracing algorithm. The two-part scouting algorithm was developed to address the field sampling problem; the coverage algorithm identifies a reasonable coverage path to traverse sampling points, while the dynamic path planning algorithm determines an optimal path between two adjacent sampling points.The developed algorithms were validated in two agricultural fields and three virtual fields by simulation. Greedy exploration policy, based on energy consumption outperformed other pattern methods in energy, time, and travel distance in the first 80% of the exploration task. The exploration strategy, which incorporated the energy consumption and the information gain with a ray tracing algorithm using a coarse map, showed an advantage over other policies in terms of the total energy consumption and the path length by at least 6%. For scouting algorithms, line sweeping methods require less energy and a shorter distance than the potential function method

Information-rich path planning under general constraints using Rapidly-exploring Random Trees

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 99-104).This thesis introduces the Information-rich Rapidly-exploring Random Tree (IRRT), an extension of the RRT algorithm that embeds information collection as predicted using Fisher information matrices. The primary contribution of this trajectory generation algorithm is target-based information maximization in general (possibly heavily constrained) environments, with complex vehicle dynamic constraints and sensor limitations, including limited resolution and narrow field-of-view. Extensions of IRRT both for decentralized, multiagent missions and for information-rich planning with multimodal distributions are presented. IRRT is distinguished from previous solution strategies by its computational tractability and general constraint characterization. A progression of simulation results demonstrates that this implementation can generate complex target-tracking behaviors from a simple model of the trade-off between information gathering and goal arrival.by Daniel S. Levine.S.M