Empirically, this optimization lead to 0.5 - 2.5% savings on expansions in our experiments A fundamental concern in real-time planning is the presence (Cserna, Gall, and Ruml 2019). of dead-ends in the state space, from which no goal is reachable. SafeRTS interleaves exploration and safety proofs during Providing real-time heuristic search algorithms that are its planning phase. As a direct consequence, it attempts complete in domains with dead-end states is a challenging safety proofs on nodes that become internal to the LSS by problem. Recently, the SafeRTS algorithm was proposed for the end of the search iteration. As shown in Cserna, Gall, and searching in such spaces (Cserna et al. 2018). SafeRTS exploits Ruml (2019), it would be equally or less difficult to achieve a user-provided predicate to identify safe states, from the same or better safety coverage by doing safety proofs after which a goal is likely reachable, and attempts to maintain a all the LSS expansions. SafeRTS has an anytime behavior backup plan for reaching a safe state at all times.

PLRTA* conflates all states that differ only in time into a single abstract state. Abstract states inherit the union of all In dynamic environments such as cities, agents often do not the predecessors of their preimage states, so that backups have time to find a complete plan to reach a goal state. Planning can be performed properly. PLRTA* learns a single static in such environment requires an agent to update its plan heuristic value for each abstract state. For dynamic learning, frequently to respond to the changes around it. The setting PLRTA* performs the standard Dijkstra-style backup across of real-time heuristic search models online planning by requiring the LSS, considering only costs arising from the dynamic elements the agent to commit to its next action within a strict of the environment. As presented by Cannon, Rose, time limit. The time bound for planning is set to the time and Ruml (2014), the algorithm commits to only one step at which the actions to which the agent has already committed along the selected path, and then replans using updated information.

Most work in heuristic search considers problems where a low cost solution is preferred (MIN problems). In this paper, we investigate the complementary setting where a solution of high reward is preferred (MAX problems). Example MAX problems include finding a longest simple path in a graph, maximal coverage, and various constraint optimization problems. We examine several popular search algorithms for MIN problems and discover the curious ways in which they misbehave on MAX problems. We propose modifications that preserve the original intentions behind the algorithms but allow them to solve MAX problems, and compare them theoretically and empirically. Interesting results include the failure of bidirectional search and close relationships between Dijkstra's algorithm, weighted A*, and depth-first search.

It is commonly appreciated that solving search problems optimally can take too long. Bounded suboptimal search algorithms trade increased solution cost for reduced solving time. Explicit Estimation Search (EES) is a recent state-of-the-art algorithm specifically designed for bounded suboptimal search. Although it tends to expand fewer nodes than alternative algorithms, such as weighted A* (WA*), its per-node expansion overhead is higher, causing it to sometimes take longer. In this paper, we present simplified variants of EES (SEES) and an earlier algorithm, A*epsilon (SA*epsilon), that use different implementations of the same motivating ideas to significantly reduce search overhead and implementation complexity. In an empirical evaluation, we find that SEES, like EES, outperforms classic bounded suboptimal search algorithms, such as WA*, on domains tested where distance-to-go estimates enable better search guidance. We also confirm that, while SEES and SA*epsilon expand roughly the same number of nodes as their progenitors, they solve problems significantly faster and are much easier to implement. This work widens the applicability of state-of the-art bounded suboptimal search by making it easier to deploy.

In many applications of shortest-path algorithms, it is impractical to find a provably optimal solution; one can only hope to achieve an appropriate balance between search time and solution cost that respects the user's preferences. Preferences come in many forms; we consider utility functions that linearly trade-off search time and solution cost. Many natural utility functions can be expressed in this form. For example, when solution cost represents the makespan of a plan, equally weighting search time and plan makespan minimizes the time from the arrival of a goal until it is achieved. Current state-of-the-art approaches to optimizing utility functions rely on anytime algorithms, and the use of extensive training data to compute a termination policy. We propose a more direct approach, called Bugsy, that incorporates the utility function directly into the search, obviating the need for a separate termination policy. We describe a new method based on off-line parameter tuning and a novel benchmark domain for planning under time pressure based on platform-style video games. We then present what we believe to be the first empirical study of applying anytime monitoring to heuristic search, and we compare it with our proposals. Our results suggest that the parameter tuning technique can give the best performance if a representative set of training instances is available. If not, then Bugsy is the algorithm of choice, as it performs well and does not require any off-line training. This work extends the tradition of research on metareasoning for search by illustrating the benefits of embedding lightweight reasoning about time into the search algorithm itself.

As of the writing of this abstract, the first stage is complete with respect to my Heuristic search is a technique used pervasively in the fields dissertation. The second stage will focus on the setting of of artificial intelligence, automated planning and operations off-line heuristic search where the entire plan is synthesized research to solve a wide range of problems from planning before any actions are executed. I will be near the completion military deployments to planning tasks for a robot that must of my work on second stage by the time of the AAAI clean a messy kitchen. An automated agent can use heuristic doctoral consortium. The third and final stage will focus on search to construct a plan that, when executed, will achieve planning under time pressure when planning and execution a desired task. The search algorithm explores different sequences of actions may happen concurrently. The remainder of this of actions that the agent can execute, looking for a abstract discusses these three topics in more detail.

Heuristic search is a central component of many important applications in AI including automated planning.  While we can find  optimal solutions to heuristic search problems, doing so may take hours or days. For practical applications, this is unacceptably slow, and we must rely on algorithms which find solutions of high, but not optimal, quality or ones which bound the time used directly. In my dissertation, I present and analyze algorithms for the following settings: quality bounded heuristic search and time  bounded heuristic search. The central theme of my doctoral work will be that taking advantage of additional information can improve the performance of heuristic search algorithms.

Bounded suboptimal search algorithms attempt to find a solution quickly while guaranteeing that the cost does not exceed optimal by more than a desired factor. These algorithms generally use a single admissible heuristic both for guidance and guaranteeing solution quality. We present a new approach to bounded suboptimal search that separates these roles, consulting multiple sources of potentially inadmissible information to determine search order and using admissible information to guarantee quality. An empirical evaluation across six benchmark domains shows the new approach has better overall performance.

Welcome to the Third International Symposium on a long line of research that was carried out in the Combinatorial Search (SoCS)! This is an important past decade by him and others about the important year for SoCS as we have established archival proceedings topic of search in nondeterministic environments. These proceedings are the Finally, we scheduled an important panel discussion result of hard work by many, from researchers to reviewers about the differences and mutual influence of domain and the publisher. Every submitted search for planning environments. Wheeler Ruml paper was assigned to three anonymous reviewers; moderates the panel, which includes three experts all experts in the topic of the paper.

My research represents an approach to integrating planning and execution in time-sensitive environments. The primary focus is on a problem called continual on-line planning. New goals arrive stochastically during execution, the agent issues actions for execution one at a time, and the environment is otherwise deterministic. My dissertation will address this setting in three stages: optimizing total goal achievement time, handling on-line goal arrival during planning or execution, and adapting to changes in state also during planning or execution. My current approach to this problem is based on incremental heuristic search. The two central issues are the decision of which partial plans to elaborate during search and the decision of when to issue an action for execution. I have proposed an extension of Russell and Wefald's decision-theoretic A* algorithm that is not limited by assumptions of an admissible heuristic like DTA*. This algorithm, Decision Theoretic On-line Continual Search (DTOCS), handles the complexities of the on-line setting by balancing deliberative planning and real-time response.