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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.
A comparison of fast search methods for real-time situated agents
- In Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume
, 2004
"... Real-time situated agents, including characters in real-time computer games, often do not know the terrain in advance but automatically observe it within a certain range around them. They have to interleave planning with movement to make planning tractable when moving autonomously to user-specified ..."
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Cited by 64 (4 self)
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Real-time situated agents, including characters in real-time computer games, often do not know the terrain in advance but automatically observe it within a certain range around them. They have to interleave planning with movement to make planning tractable when moving autonomously to user-specified coordinates. Planning faces real-time requirements since it is important that the agents be responsive to the commands of the users and move smoothly. In this paper, we compare two fast search methods for this task that speed up planning in different ways, namely realtime heuristic search (LRTA*) and incremental heuristic search (D * Lite), resulting in the first comparison of real-time and incremental heuristic search in the literature. We characterize when to choose which search method, depending on the kind of terrain and the planning objective.
Agent-centered search
- Articial Intelligence Magazine
"... In this article, we describe agent-centered search (sometimes 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 sta ..."
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Cited by 55 (5 self)
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In this article, we describe agent-centered search (sometimes 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. They 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
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.
State abstraction in real-time heuristic search
- Journal of Artificial Intelligence Research
, 2006
"... Real-time heuristic search methods are used by situated agents in applications that require the amount of planning per move to be constant-bounded regardless of the problem size. Such agents plan only a few actions in a local search space and avoid getting trapped in heuristic local minima by improv ..."
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Cited by 34 (10 self)
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Real-time heuristic search methods are used by situated agents in applications that require the amount of planning per move to be constant-bounded regardless of the problem size. Such agents plan only a few actions in a local search space and avoid getting trapped in heuristic local minima by improving their heuristic function over time. We extend a wide class of real-time search algorithms with automatically built graph abstraction. Extensive empirical evaluation in the domain of goaldirected navigation demonstrates that the use of abstraction accelerates learning of the heuristic function while maintaining real-time performance. The resulting algorithm outperforms virtually all tested algorithms simultaneously along negatively correlated performance measures.
Learning in real-time search: A unifying framework
- Journal of Artificial Intelligence Research
, 2006
"... Real-time search methods are suited for tasks in which the agent is interacting with an initially unknown environment in real time. In such simultaneous planning and learning problems, the agent has to select its actions in a limited amount of time, while sensing only a local part of the environment ..."
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Cited by 23 (1 self)
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Real-time search methods are suited for tasks in which the agent is interacting with an initially unknown environment in real time. In such simultaneous planning and learning problems, the agent has to select its actions in a limited amount of time, while sensing only a local part of the environment centered at the agent’s current location. Real-time heuristic search agents select actions using a limited lookahead search and evaluating the frontier states with a heuristic function. Over repeated experiences, they refine heuristic values of states to avoid infinite loops and to converge to better solutions. The wide spread of such settings in autonomous software and hardware agents has led to an explosion of real-time search algorithms over the last two decades. Not only a potential user is confronted with a hodgepodge of algorithms but also he faces the choice of control parameters they use. In this paper we address both problems. The first contribution is an introduction of a simple three-parameter framework (named LRTS) which extracts the core ideas behind many existing algorithms. We then prove that LRTA*, ɛ-LRTA * , SLA*, and γ-Trap algorithms are special cases of our framework. Thus, they are unified and extended with additional features. Second, we prove completeness and convergence of any algorithm covered by the LRTS framework. Third, we prove several upper-bounds relating the control parameters and solution quality. Finally, we analyze the influence of the three control parameters empirically in the realistic scalable domains of real-time navigation on initially unknown maps from a commercial role-playing game as well as routing in ad hoc sensor networks.
Improving LRTA*(k)
, 2007
"... We identify some weak points of the LRTA*(k) algorithm in the propagation of heuristic changes. To solve them, we present a new algorithm, LRTA*LS(k), that is based on the selection and updating of the interior states of a local space around the current state. It keeps the good theoretical propertie ..."
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Cited by 22 (9 self)
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We identify some weak points of the LRTA*(k) algorithm in the propagation of heuristic changes. To solve them, we present a new algorithm, LRTA*LS(k), that is based on the selection and updating of the interior states of a local space around the current state. It keeps the good theoretical properties of LRTA*(k), while improving substantially its performance. It is related with a lookahead depth greater than 1. We provide experimental evidence of the benefits of the new algorithm on real-time benchmarks with respect to existing approaches.
Speeding up learning in real-time search via automatic state abstraction
- In Proceedings of the National Conference on Artificial Intelligence (AAAI
, 2005
"... Situated agents which use learning real-time search are well poised to address challenges of real-time path-finding in robotic and computer game applications. They interleave a local lookahead search with movement execution, explore an initially unknown map, and converge to better paths over repeate ..."
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Cited by 22 (8 self)
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Situated agents which use learning real-time search are well poised to address challenges of real-time path-finding in robotic and computer game applications. They interleave a local lookahead search with movement execution, explore an initially unknown map, and converge to better paths over repeated experiences. In this paper, we first investigate how three known extensions of the most popular learning real-time search algorithm (LRTA*) influence its performance in a path-finding domain. Then, we combine automatic state abstraction with learning real-time search. Our scheme of dynamically building a state abstraction allows us to generalize updates to the heuristic function, thereby speeding up learning. The novel algorithm converges up to 80 times faster than LRTA * with only one fifth of the response time of A*. 1
Real-time heuristic search with a priority queue
- In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI
, 2007
"... Learning real-time search, which interleaves planning and acting, allows agents to learn from multiple trials and respond quickly. Such algorithms require no prior knowledge of the environment and can be deployed without pre-processing. We introduce Prioritized-LRTA * (P-LRTA*), a learning real-time ..."
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Cited by 19 (9 self)
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Learning real-time search, which interleaves planning and acting, allows agents to learn from multiple trials and respond quickly. Such algorithms require no prior knowledge of the environment and can be deployed without pre-processing. We introduce Prioritized-LRTA * (P-LRTA*), a learning real-time search algorithm based on Prioritized Sweeping. P-LRTA * focuses learning on important areas of the search space, where the importance of a state is determined by the magnitude of the updates made to neighboring states. Empirical tests on path-planning in commercial game maps show a substantial learning speed-up over state-of-the-art real-time search algorithms. 1
Planning under Partial Observability by Classical Replanning: Theory and Experiments
"... Planning with partial observability can be formulated as a non-deterministic search problem in belief space. The problem is harder than classical planning as keeping track of beliefs is harder than keeping track of states, and searching for action policies is harder than searching for action sequenc ..."
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Cited by 19 (5 self)
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Planning with partial observability can be formulated as a non-deterministic search problem in belief space. The problem is harder than classical planning as keeping track of beliefs is harder than keeping track of states, and searching for action policies is harder than searching for action sequences. In this work, we develop a framework for partial observability that avoids these limitations and leads to a planner that scales up to larger problems. For this, the class of problems is restricted to those in which 1) the non-unary clauses representing the uncertainty about the initial situation are invariant, and 2) variables that are hidden in the initial situation do not appear in the body of conditional effects, which are all assumed to be deterministic. We show that such problems can be translated in linear time into equivalent fully observable non-deterministic planning problems, and that an slight extension of this translation renders the problem solvable by means of classical planners. The whole approach is sound and complete provided that in addition, the state-space is connected. Experiments are also reported. 1
