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Real-Time Adaptive A*.
- In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems,
, 2006
"... ABSTRACT Agents often have to solve series of similar search problems. Adaptive A* is a recent incremental heuristic search algorithm that solves series of similar search problems faster than A* because it updates the h-values using information from previous searches. It basically transforms consis ..."
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Cited by 80 (15 self)
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ABSTRACT Agents often have to solve series of similar search problems. Adaptive A* is a recent incremental heuristic search algorithm that solves series of similar search problems faster than A* because it updates the h-values using information from previous searches. It basically transforms consistent hvalues into more informed consistent h-values. This allows it to find shortest paths in state spaces where the action costs can increase over time since consistent h-values remain consistent after action cost increases. However, it is not guaranteed to find shortest paths in state spaces where the action costs can decrease over time because consistent h-values do not necessarily remain consistent after action cost decreases. Thus, the h-values need to get corrected after action cost decreases. In this paper, we show how to do that, resulting in Generalized Adaptive A* (GAA*) that finds shortest paths in state spaces where the action costs can increase or decrease over time. Our experiments demonstrate that Generalized Adaptive A* outperforms breadth-first search, A* and D* Lite for moving-target search, where D* Lite is an alternative state-of-the-art incremental heuristic search algorithm that finds shortest paths in state spaces where the action costs can increase or decrease over time.
Comparing Real-Time and Incremental Heuristic Search for Real-Time Situated Agents
, 2009
"... Real-time situated agents, such as characters in real-time computer games, often do not know the terrain in advance but automatically observe it within a certain range around themselves. They have to interleave searches with action executions to make the searches tractable when moving autonomously t ..."
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Cited by 36 (2 self)
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Real-time situated agents, such as characters in real-time computer games, often do not know the terrain in advance but automatically observe it within a certain range around themselves. They have to interleave searches with action executions to make the searches tractable when moving autonomously to user-specified coordinates. The searches face real-time requirements since it is important that the agents be responsive to the commands of the users and move smoothly. In this article, we compare two classes of fast heuristic search methods for these navigation tasks that speed up A * searches in different ways, namely real-time heuristic search and incremental heuristic search, to understand their advantages and disadvantages and make recommendations about when each one should be used. We first develop a competitive real-time heuristic search method. LSS-LRTA * is a version of Learning Real-Time A* that uses A * to determine its local search spaces and learns quickly. We analyze the properties of LSS-LRTA * and then compare it experimentally against the state-of-the-art incremental heuristic search method D * Lite [21] on our navigation tasks, for which D * Lite was specifically developed, resulting in the first comparison of real-time and incremental heuristic search in the literature. We characterize when to choose each one of the two heuristic search methods, depending on the search objective and the kind of terrain. Our experimental results show that LSS-LRTA * can outperform D * Lite under the right conditions, namely when there is time pressure or the user-supplied h-values are generally not misleading.
Speeding up moving-target search
- In Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
, 2007
"... In this paper, we study moving-target search, where an agent (= hunter) has to catch a moving target ( = prey). The agent does not necessarily know the terrain initially but can observe it within a certain sensor range around itself. It uses the strategy to always move on a shortest presumed unblock ..."
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Cited by 16 (6 self)
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In this paper, we study moving-target search, where an agent (= hunter) has to catch a moving target ( = prey). The agent does not necessarily know the terrain initially but can observe it within a certain sensor range around itself. It uses the strategy to always move on a shortest presumed unblocked path toward the target, which is a reasonable strategy for computer-controlled characters in video games. We study how the agent can find such paths faster by exploiting the fact that it performs A * searches repeatedly. To this end, we extend Adaptive A*, an incremental heuristic search method, to moving-target search and demonstrate experimentally that the resulting MT-Adaptive A * is faster than isolated A * searches and, in many situations, also D * Lite, a state-of-the-art incremental heuristic search method. In particular, it is faster than D * Lite by about one order of magnitude for moving-target search in known and initially unknown mazes if both search methods use the same informed heuristics. Categories and Subject Descriptors
The fringe-saving A* search algorithm - a feasibility study
- In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI
"... In this paper, we develop Fringe-Saving A* (FSA*), an incremental version of A * that repeat-edly finds shortest paths in a known gridworld from a given start cell to a given goal cell while the traversability costs of cells increase or decrease. The first search of FSA * is the same as that of A*. ..."
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Cited by 12 (3 self)
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In this paper, we develop Fringe-Saving A* (FSA*), an incremental version of A * that repeat-edly finds shortest paths in a known gridworld from a given start cell to a given goal cell while the traversability costs of cells increase or decrease. The first search of FSA * is the same as that of A*. However, FSA * is able to find shortest paths dur-ing the subsequent searches faster than A * because it reuses the beginning of the immediately preceed-ing A * search tree that is identical to the current A* search tree. FSA * does this by restoring the content of the OPEN list of A * at the point in time when an A * search for the current search problem could de-viate from the A * search for the immediately pre-ceeding search problem. We present first experi-mental results that demonstrate that FSA * can have a runtime advantage over A * and Lifelong Planning A * (LPA*), an alternative incremental version of A*. 1
Avoiding and Escaping Depressions in Real-Time Heuristic Search
"... Heuristics used for solving hard real-time search problems have regions with depressions. Such regions are bounded areas of the search space in which the heuristic function is inaccurate compared to the actual cost to reach a solution. Early real-time search algorithms, like LRTA ∗ , easily become t ..."
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Cited by 10 (3 self)
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Heuristics used for solving hard real-time search problems have regions with depressions. Such regions are bounded areas of the search space in which the heuristic function is inaccurate compared to the actual cost to reach a solution. Early real-time search algorithms, like LRTA ∗ , easily become trapped in those regions since the heuristic values of their states may need to be updated multiple times, which results in costly solutions. State-of-the-art real-time search algorithms, like LSS-LRTA ∗ or LRTA ∗ (k), improve LRTA ∗ ’s mechanism to update the heuristic, resulting in improved performance. Those algorithms, however, do not guide search towards avoiding depressed regions. This paper presents depression avoidance, a simple real-time search principle to guide search towards avoiding states that have been marked as part of a heuristic depression. We propose two ways in which depression avoidance can be implemented: mark-and-avoid and move-to-border. We implement these strategies on top of LSS-LRTA ∗ and RTAA ∗ , producing 4 new real-time heuristic search algorithms: aLSS-LRTA ∗ , daLSS-LRTA ∗ , aRTAA ∗ , and daRTAA ∗. When the objective is to find a single solution by running the real-time search algorithm once, we show that daLSS-LRTA ∗ and daRTAA ∗ outperform their predecessors sometimes by one order of magnitude. Of the four new algorithms, daRTAA ∗ produces the best solutions given a fixed deadline on the average time allowed per planning episode. We prove all our algorithms have good theoretical properties: in finite search spaces, they find a solution if one exists, and converge to an optimal after a number of trials. 1.
Creating a uniform framework for task and motion planning: A case for incremental heuristic search
- In ICAPS
, 2010
"... In this short overview paper, we describe our vision for com-bining task and motion planning and present a historical per-spective to show which parts of it have already become re-ality. Robots do not have to plan only once but repeatedly. Replanning from scratch is often very time consuming. In-cre ..."
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Cited by 1 (0 self)
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In this short overview paper, we describe our vision for com-bining task and motion planning and present a historical per-spective to show which parts of it have already become re-ality. Robots do not have to plan only once but repeatedly. Replanning from scratch is often very time consuming. In-cremental heuristic search addresses this issue by reusing in-formation from previous searches to find solutions to series of similar search tasks often much faster than is possible by solving each search task from scratch. Incremental heuris-tic search has mostly been used for path planning in the past but we argue that it applies to many layers of robot architec-tures, in particular task and motion planning, which might al-low one to design very homogeneous robot architectures with clean interfaces between the layers.
Path-Adaptive A* for incremental heuristic search in unknown terrain
- IN PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON AUTOMATED PLANNING AND SCHEDULING
, 2009
"... Adaptive A * is an incremental version of A * that up-dates the h-values of the previous A * search to make them more informed and thus future A * searches more focused. In this paper, we show how the A * searches performed by Adaptive A * can reuse part of the path of the previous search and termin ..."
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Adaptive A * is an incremental version of A * that up-dates the h-values of the previous A * search to make them more informed and thus future A * searches more focused. In this paper, we show how the A * searches performed by Adaptive A * can reuse part of the path of the previous search and terminate before they expand a goal state, resulting in Path-Adaptive A*. We demonstrate experimentally that Path-Adaptive A * expands fewer states per search and runs faster than Adaptive A * when solving path-planning problems in initially unknown terrain.
