Moving Target Search Research Articles

Moving Target Search Algorithm with Informational Distance Measures

We consider an Ishida and Korf Moving Target Search (MTS) algorithm with informational distance measures. Similarly to the previously defined Informational Learning Real-Time A* algorithm, the suggested algorithm acts on the set of partitions of the sample space, on which the probability mass function is defined. The information-based Rokhlin metric and its lower bound - Ornstein metric, give the necessary distance measures. We prove that similarly to the Ishida and Korf MTS algorithm, the proposed Informational MTS (IMTS) algorithm always terminates and finds the target. The comparison of the IMTS algorithm with known models shows that it outperforms known Markov decision process model of search with probabilistic and informational decision criteria. These findings help to construct a unified framework of search after both static and moving targets, and to bridge the gap between different search procedures that are related to both artificial intelligence and information theory.

국방 분야의 인간-차량 인터랙션 연구

We present recent human-vehicle interaction (HVI) research conducted in the area of defense and military application. Research topics discussed in this paper include: training simulation for overland navigation tasks; expertise effects in overland navigation performance and scan patterns; pilot’s perception and confidence on an overland navigation task; effects of UAV (Unmanned Aerial Vehicle) supervisory control on F-18 formation flight performance in a simulator environment; autonomy balancing in a manned-unmanned teaming (MUT) swarm attack, enabling visual detection of IED (Improvised Explosive Device) indicators through Perceptual Learning Assessment and Training; usability test on DaViTo (Data Visualization Tool); and modeling peripheral vision for moving target search and detection. Diverse and leading HVI study in the defense domain suggests future research direction in other HVI emerging areas such as automotive industry and aviation domain.

Modeling Peripheral Vision for Moving Target Search and Detection

Most target search and detection models focus on foveal vision. In reality, peripheral vision plays a significant role, especially in detecting moving objects. There were 23 subjects who participated in experiments simulating target detection tasks in urban and rural environments while their gaze parameters were tracked. Button responses associated with foveal object and peripheral object (PO) detection and recognition were recorded. In an urban scenario, pedestrians appearing in the periphery holding guns were threats and pedestrians with empty hands were non-threats. In a rural scenario, non-U.S. unmanned aerial vehicles (UAVs) were considered threats and U.S. UAVs non-threats. On average, subjects missed detecting 2.48 POs among 50 POs in the urban scenario and 5.39 POs in the rural scenario. Both saccade reaction time and button reaction time can be predicted by peripheral angle and entrance speed of POs. Fast moving objects were detected faster than slower objects and POs appearing at wider angles took longer to detect than those closer to the gaze center. A second-order mixed-effect model was applied to provide each subject's prediction model for peripheral target detection performance as a function of eccentricity angle and speed. About half the subjects used active search patterns while the other half used passive search patterns. An interactive 3-D visualization tool was developed to provide a representation of macro-scale head and gaze movement in the search and target detection task. An experimentally validated stochastic model of peripheral vision in realistic target detection scenarios was developed.

Incremental ARA*: An Incremental Anytime Search Algorithm for Moving-Target Search

Moving-target search, where a hunter has to catch a moving target, is an important problem for video game developers. In our case, the hunter repeatedly moves towards the target and thus has to solve similar search problems repeatedly. We develop Incremental ARA* (I-ARA*) for this purpose, the first incremental anytime search algorithm for moving-target search in known terrain. We provide an error bound on the lengths of the paths found by I-ARA* and show experimentally in known four-neighbor gridworlds that I-ARA* can be used with smaller time limits between moves of the hunter than competing state-of-the-art moving-target search algorithms, namely repeated A*, G-FRA*, FRA*, and sometimes repeated ARA*. The hunter tends to make more moves with I-ARA* than repeated A*, G-FRA* or FRA*, which find shortest paths for the hunter, but fewer moves with I-ARA* than repeated ARA*, which finds suboptimal paths for the hunter like I-ARA*. Also, the error bounds on the lengths of the paths of the hunter tend to be smaller with I-ARA* than repeated ARA*.

ROUTE PLANNING AND OPTIMIZATION OF ROUTE USING SIMULATED ANT AGENT SYSTEM

This research presents an optimization technique for route planning using simulated ant agents for dynamic online route planning and optimization of the route. It addresses the issues involved during route planning in dynamic and unknown environments cluttered with obstacles and objects. A simulated ant agent system (SAAS) is proposed using modified ant colony optimization algorithm for dealing with online route planning. It is compared with evolutionary technique on randomly generated environments, obstacle ratio, grid sizes, and complex environments. The evolutionary technique performs well in simple and less cluttered environments while its performance degrades with large and complex environments. The SAAS generates and optimizes routes in complex and large environments with constraints. The traditional route optimization techniques focus on good solutions only and do not exploit the solution space completely. The SAAS is shown to be an efficient technique for providing safe, short, and feasible routes under dynamic constraints and its efficiency has been tested in a mine field simulation with different environment configurations and is capable of tracking the moving goal and performs equally well as compared to moving target search algorithm.

Moving-Target Pursuit Algorithm Using Improved Tracking Strategy

Pursuing a moving target in modern computer games presents several challenges to situated agents, including real-time response, large-scale search space, severely limited computation resources, incomplete environmental knowledge, adversarial escaping strategy, and outsmarting the opponent. In this paper, we propose a novel tracking automatic optimization moving-target pursuit (TAO-MTP) algorithm employing improved tracking strategy to effectively address all challenges above for the problem involving single hunter and single prey. TAO-MTP uses a queue to store prey's trajectory, and simultaneously runs real-time adaptive A* (RTAA*) repeatedly to approach the optimal position updated periodically in the trajectory within limited steps, which makes the overall pursuit cost smallest. In the process, the hunter speculatively moves to any position explored in the trajectory, not necessarily the optimal position, to speed up convergence, and then directly moves along the trajectory to pursue the prey. Moreover, automatic optimization methods, such as reducing trajectory storage and optimizing pursuit path, are used to further enhance its performance. As long as the hunter's moving speed is faster than that of the prey, and its sense scope is large enough, it will eventually capture the prey. Experiments using commercial game maps show that TAO-MTP is independent of adversarial escaping strategy, and outperforms all the classic and state-of-the-art moving-target pursuit algorithms such as extended moving-target search (eMTS), path refinement moving-target search (PR MTS), moving-target adaptive A* (MTAA*), and generalized adaptive A* (GAA*).

Novel moving target search algorithms for computer gaming

In a computer game, equipping a bot with a suitable algorithm to locate a human player is difficult. Besides the unpredictable moves made by the player, an unexplored map region poses additional constraints such as new obstacles and pathways that the bot needs to discover quickly. The design criteria of such moving target search (MTS) algorithms would typically need to consider computation efficiency and storage requirements. That is, the bot must appear to be “smart” and “quick” in order to enhance the playability and challenge posed by the game. These criteria, however, pose conflicting requirements. In this article, we study and evaluate the performance and behavior of two novel MTS algorithms, Fuzzy MTS and Abstraction MTS, against existing MTS algorithms in randomly generated mazes of increasing size. Simulations reveal that Fuzzy MTS and Abstraction MTS exhibit competitive performance even with large problem spaces.

Real-Time Moving Target Evaluation Search

In this correspondence, we address the problem of real-time moving target search in dynamic and partially observable environments, and propose an algorithm called real-time moving target evaluation search (MTES). MTES is able to detect the closed directions around the agent and determines the estimated best direction to capture a moving target avoiding the obstacles nearby. We have also developed a new prey algorithm (Prey-A*) to test the existing and our predator algorithms in our experiments. We have obtained an impressive improvement over moving target search, real-time target evaluation search, and real-time edge follow with respect to path length. Furthermore, we have also tested our algorithm against A*.

Performance Simulations of Moving Target Search Algorithms

The design of appropriate moving target search (MTS) algorithms for computer-generated bots poses serious challenges as they have to satisfy stringent requirements that include computation and execution efficiency. In this paper, we investigate the performance and behaviour of existing moving target search algorithms when applied to search-and-capture gaming scenarios. As part of the investigation, we also introduce a novel algorithm known as abstraction MTS. We conduct performance simulations with a game bot and moving target within randomly generated mazes of increasing sizes and reveal that abstraction MTS exhibits competitive performance even with large problem spaces.

Discounted MEAN bound for the optimal searcher path problem with non-uniform travel times

We consider an extension of the optimal searcher path problem (OSP), where a searcher moving through a discretised environment may now need to spend a non-uniform amount of time travelling from one region to another before being able to search it for the presence of a moving target. In constraining not only where but when the search of each cell can take place, the problem more appropriately models the search of environments which cannot be easily partitioned into equally sized cells. An existing OSP bounding method in literature, the MEAN bound, is generalised to provide bounds for solving the new problem in a branch and bound framework. The main contribution of this paper is an enhancement, discounted MEAN (DMEAN), which greatly tightens the bound for the new and existing problems alike with almost no additional computation. We test the new algorithm against existing OSP bounding methods and show it leads to faster solution times for moving target search problems.

Optimal search-relocation trade-off in Markovian-target searching

In this study, a standard moving-target search model was extended with a multiple-search-speed option, whereby a trade-off is enabled between the increased detection chances owing to the searcher's better location and the increased uncertainty of the target's location resulting from the diminished search performance incurred in the relocation. This enhances the detection probability of the output search path and, thereby, the model's practicality. However, the scalability of the solution method is essential to its implementation, as the basic model is already NP-hard. We developed an efficient heuristic by combining the idea of approximate nondetection probability minimization and a hybridized shortest-path heuristic that exploits the fast-mixing property of the Markov chain. According to the results of an intensive experiment, the heuristic achieves a near-optimal trade-off within a very reasonable computation time.

Agent-centered search

In this article, I describe agent-centered search (also called real-time search or local search) and illustrate this planning paradigm with examples. Agent-centered search methods interleave planning and plan execution and restrict planning to the part of the domain around the current state of the agent, for example, the current location of a mobile robot or the current board position of a game. These methods can execute actions in the presence of time constraints and often have a small sum of planning and execution cost, both because they trade off planning and execution cost and because they allow agents to gather information early in nondeterministic domains, which reduces the amount of planning they have to perform for unencountered situations. These advantages become important as more intelligent systems are interfaced with the world and have to operate autonomously in complex environments. Agent-centered search methods have been applied to a variety of domains, including traditional search, strips-type planning, moving-target search, planning with totally and partially observable Markov decision process models, reinforcement learning, constraint satisfaction, and robot navigation. I discuss the design and properties of several agent-centered search methods, focusing on robot exploration and localization.

Visual search for motion-form conjunctions: is form discriminated within the motion system?

Motion-form conjunction search can be more efficient when the target is moving (a moving 45 degrees tilted line among moving vertical and stationary 45 degrees tilted lines) rather than stationary. This asymmetry may be due to aspects of form being discriminated within a motion system representing only moving items, whereas discrimination of stationary items relies on a static form system (J. Driver & P. McLeod, 1992). Alternatively, it may be due to search exploiting differential motion velocity and direction signals generated by the moving-target and distractor lines. To decide between these alternatives, 4 experiments systematically varied the motion-signal information conveyed by the moving target and distractors while keeping their form difference salient. Moving-target search was found to be facilitated only when differential motion-signal information was available. Thus, there is no need to assume that form is discriminated within the motion system.

Real-Time Search for Autonomous Agents and Multiagent Systems

Since real-time search provides an attractive framework for resource-bounded problem solving, this paper extends the framework for autonomous agents and for a multiagent world. To adaptively control search processes, we propose e-search which allows suboptimal solutions with e error, and δ-search which balances the tradeoff between exploration and exploitation. We then consider search in uncertain situations, where the goal may change during the course of the search, and propose a moving target search (MTS) algorithm. We also investigate real-time bidirectional search (RTBS) algorithms, where two problem solvers cooperatively achieve a shared goal. Finally, we introduce a new problem solving paradigm, called organizational problem solving, for multiagent systems.

Using multiple searchers in constrained‐path, moving‐target search problems

The search theory open literature has paid little, if any, attention to the multiple-searcher, moving-target search problem. We develop an optimal branch-and-bound procedure and six heuristics for solving constrained-path problems with multiple searchers. Our optimal procedure outperforms existing approaches when used with only a single searcher. For more than one searcher, the time needed to guarantee an optimal solution is prohibitive. Our heuristics represent a wide variety of approaches: One solves partial problems optimally, two use paths based on maximizing the expected number of detections, two are genetic algorithm implementations, and one is local search with random restarts. A heuristic based on the expected number of detections obtains solutions within 2% of the best known for each one-, two-, and three-searcher test problem considered. For one- and two-searcher problems, the same heuristic's solution time is less than that of other heuristics. For three-searcher problems, a genetic algorithm implementation obtains the best-known solution in as little as 20% of other heuristic solution times. © 1996 John Wiley & Sons, Inc.

Using multiple searchers in constrained-path, moving-target search problems

The search theory open literature has paid little, if any, attention to the multiple-searcher, moving-target search problem. We develop an optimal branch-and-bound procedure and six heuristics for solving constrained-path problems with multiple searchers. Our optimal procedure outperforms existing approaches when used with only a single searcher. For more than one searcher, the time needed to guarantee an optimal solution is prohibitive. Our heuristics represent a wide variety of approaches: One solves partial problems optimally, two use paths based on maximizing the expected number of detections, two are genetic algorithm implementations, and one is local search with random restarts. A heuristic based on the expected number of detections obtains solutions within 2% of the best known for each one-, two-, and three-searcher test problem considered. For one- and two-searcher problems, the same heuristic's solution time is less than that of other heuristics. For three-searcher problems, a genetic algorithm implementation obtains the best-known solution in as little as 20% of other heuristic solution times.

Moving-target search: a real-time search for changing goals

Considers the case of heuristic search where the goal may change during the course of the search. For example, the goal may be a target that actively avoids the problem solver. The authors present a moving-target search algorithm (MTS) to solve this problem. The authors prove that if the average speed of the target is slower than that of the problem solver, then the problem solver is guaranteed to eventually reach the target in a connected problem space. The original MTS algorithm was constructed with the minimum operations necessary to guarantee its completeness, and hence is not very efficient. To improve its efficiency, the authors introduce ideas from the area of resource-bounded planning into MTS, including 1) commitment to goals, and 2) deliberation for selecting plans. Experimental results demonstrate that the improved MTS is 10 to 20 times more efficient than the original MTS in uncertain situations.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Criteria and approximate methods for path-constrained moving-target search problems

A search is conducted for a target moving in discrete time among a finite number of cells according to a known Markov process. The searcher must choose one cell in which to search in each time period. The set of cells available for search depends upon the cell chosen in the last time period. The problem is to find a search path, i.e., a sequence of search cells, that either maximizes the probability of detection or minimizes the mean number of time periods required for detection. The search problem is modelled as a partially observable Markov decision process and several approximate solutions procedures are proposed. © 1995 John Wiley & Sons, Inc.

An Optimal Branch-and-Bound Procedure for the Constrained Path, Moving Target Search Problem

A searcher and target move among a finite set of cells C = 1, 2, …,N in discrete time. At the beginning of each time period, one cell is searched. If the target is in the selected cell j, it is detected with probability qj. If the target is not in the cell searched, it cannot be detected during the current time period. After each search, a target in cell j moves to cell k with probability pjk. The target transition matrix, P = [pjk] is known to the searcher. The searcher's path is constrained in that if the searcher is currently in cell j, the next search cell must be selected from a set of neighboring cells Cj. The object of the search is to minimize the probability of not detecting the target in T searches.