Heuristic Search When Time Matters

Abstract

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-theart 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.

Bounded Rationality, Abstraction, and Hierarchical Decision-Making: An Information-Theoretic Optimality Principle

“…Following the pioneering work of Simon (1955Simon ( , 1972 on bounded rationality, decision-making with limited information-processing resources has been studied extensively in psychology (Gigerenzer and Todd, 1999;Camerer, 2003;Brighton, 2009), economics (McKelvey andPalfrey, 1995;Rubinstein, 1998;Kahneman, 2003;Parkes and Wellman, 2015), political science (Jones, 2003), industrial organization (Spiegler, 2011), cognitive science (Howes et al, 2009;Janssen et al, 2011), computer science, and artificial intelligence research (Horvitz, 1988;Lipman, 1995;Russell, 1995;Russell and Subramanian, 1995;Russell and Norvig, 2002;Lewis et al, 2014). Conceptually, the approaches differ widely ranging from heuristics (Tversky and Kahneman, 1974;Gigerenzer and Todd, 1999;Gigerenzer and Brighton, 2009;Burns et al, 2013) to approximate statistical inference schemes (Levy et al, 2009;Vul et al, 2009Vul et al, , 2014Sanborn et al, 2010;Tenenbaum et al, 2011;Fox and Roberts, 2012;Lieder et al, 2012).…”

Achieving Goals Quickly Using Real-time Search: Experimental Results in Video Games

“…We compare the different techniques on the platform pathfinding benchmark of Burns et al (2013b). This domain is inspired by popular platform-based video games like Super Mario Bros.…”

“…For our experiments we used 25 test instances created using the level generator described by Burns et al (2013b), where the maze for each instance was unique and had a random start and goal location. We used the offline training techniques described above to learn the amount of time required to perform different amounts of lookahead search.…”

“…Bugsy (Burns et al, 2013b) is not a real-time search, but it is an off-line algorithm that explicitly attempts to optimize a utility function given as a linear combination of search time and solution cost. If solution cost is specified in units of time, then Bugsy can explicitly attempt to minimize goal achievement time by appropriately weighting search and execution times so that they are given in the same units.…”

“…We explore the use of real-time heuristic search on two additional domains that more closely reflect features of dynamic application domains, such as robotics. One is a platform-based pathfinding domain proposed by Burns, Ruml, and Do (2013b), and the other is a novel domain that we call the traffic problem, featuring navigation through a c 2015 AI Access Foundation. All rights reserved.…”