Meta-Level Techniques for Planning, Search, and Scheduling

Metareasoning is a core idea in AI at that captures the essence of being both human and intelligent. This idea is that much can be gained by thinking (reasoning) about one's own thinking. In the context of search and planning, metareasoning concerns with making explicit decisions about computation steps, by comparing their `cost' in computational resources, against the gain they can be expected to make towards advancing the search for solution (or plan) and thus making better decisions. To apply metareasoning, a meta-level problem needs to be defined and solved with respect to a specific framework or algorithm. In some cases, these meta-level problems can be very hard to solve. Yet, even a fast-to-compute approximation of meta-level problems can yield good results and improve the algorithms to which they are applied. This paper provides an overview of different settings in which we applied metareasoning to improve search, planning and scheduling

The Closed List Is an Obstacle Too

The baseline approach for optimal path finding in 4-connected grids is A* with Manhattan Distance. Nevertheless, a large number of enhancements were suggested over the years, usually requiring a preprocessing phase and/or additional memory to store smart lookup tables. In this paper we introduce an enhancement to A* (called BOXA*) on grids which does not need any preprocessing and only needs negligible additional memory. The main idea is to treat the closed-list as a dynamic obstacle. We maintain a list of rectangles which surround CLOSED nodes and calculate an admissible heuristic using the fact that an optimal path from a given node must go around these rectangles. We experimentally show the benefits of this approach on a variety of grid domains

Metareasoning for Interleaved Planning and Execution

Agents that plan and act in the real world must deal with the fact that time passes as they are planning. In the presence of tight deadlines, there may be insufficient time to complete the search for a plan before it is time to act. One can gain additional time to search by starting to act before a complete plan is found, incurring the risk of making incorrect action choices. This tradeoff between opportunity and risk, inherent in interleaving planning and execution, is a non-trivial metareasoning problem addressed in this paper

Algorithm Selection in Optimization and Application to Angry Birds

Consider the MaxScore algorithm selection problem: given some optimization problem instances, a set of algorithms that solve them, and a time limit, what is the optimal policy for selecting (algorithm, instance) runs so as to maximize the sum of solution qualities for all problem instances?We analyze the computational complexity of restrictions of MaxScore (NP-hard), and provide a dynamic programming approximation algorithm. This algorithm, as well as new greedy algorithms, are evaluated empirically on data from agent runs on Angry Birds problem instances. Results show a significant improvement over a hyper-agent greedy scheme from related work.Solomon Eyal ShimonyAvinoam Yehezke

Iterative-deepening Bidirectional Heuristic Search with Restricted Memory

The field of bidirectional heuristic search has recently seen great advances. However, the subject of memory-restricted bidirectional search has not received recent attention. In this paper we introduce a general iterative deepening bidirectional heuristic search algorithm (IDBiHS) that searches simultaneously in both directions while controlling the meeting point of the search frontiers. First, we present the basic variant of IDBiHS, whose memory is linear in the search depth. We then add improvements that exploit consistency and front-to-front heuristics. Next, we move to the case where a fixed amount of memory is available to store nodes during the search and develop two variants of IDBiHS: (1) A*+IDBiHS, that starts with A* and moves to IDBiHS as soon as memory is exhausted. (2) A variant that stores partial forward frontiers until memory is exhausted and then tries to match each of them from the backward side. Finally, we experimentally compare the new algorithms to existing unidirectional and bidirectional ones. In many cases our new algorithms outperform previous ones in both node expansions and time

Iterative-Deepening Bidirectional Heuristic Search with Restricted Memory

This extended abstract presents a bidirectional heuristic search algorithm called IDBiHS that operates under restricted memory. Several variants of this algorithm are introduced for different types of memory restrictions, and are compared against existing algorithms with similar restrictions

A Formal Metareasoning Model of Concurrent Planning and Execution

Agents that plan and act in the real world must deal with the fact that time passes as they are planning. When timing is tight, there may be insufficient time to complete the search for a plan before it is time to act. By commencing execution before search concludes, one gains time to search by making planning and execution concurrent. However, this incurs the risk of making incorrect action choices, especially if actions are irreversible. This tradeoff between opportunity and risk is the problem addressed in this paper. Our main contribution is to formally define this setting as an abstract metareasoning problem. We find that the abstract problem is intractable. However, we identify special cases that are solvable in polynomial time, develop greedy solution algorithms, and, through tests on instances derived from search problems, find several methods that achieve promising practical performance. This work lays the foundation for a principled time-aware executive that concurrently plans and executes

W-restrained Bidirectional Bounded-Suboptimal Heuristic Search

In this paper, we develop theoretical foundations for bidirectional bounded-suboptimal search (BiBSS) based on recent advancements in optimal bidirectional search. In addition, we introduce a BiBSS variant of the prominent meet-in-the-middle (MM) algorithm, called Weighted MM (WMM). We show that WMM has an interesting property of being W-restrained, and study it empirically

When to Commit to an Action in Online Planning and Search

In online planning, search is concurrent with execution. Under the formulation of planning as heuristic search, when a planner commits to an action, it re-roots its search tree at the node representing the outcome of that action. For the system to remain controlled, the planner must commit to a new action (perhaps a no-op) before the previously chosen action completes. This time pressure results in a real-time search. In this time-bounded setting, it can be beneficial to commit early, in order to perform more lookahead search focused below an upcoming state. In this paper, we propose a principled method for making this commitment decision. Our experimental evaluation shows that our scheme can outperform previously-proposed fixed strategies

Situated Temporal Planning Using Deadline-aware Metareasoning

We address the problem of situated temporal planning, in which an agent's plan can depend on scheduled exogenous events, and thus it becomes important to take the passage of time into account during the planning process. Previous work on situated temporal planning has proposed simple pruning strategies, as well as complex schemes for a simplified version of the associated metareasoning problem. Although even the simplified version of the metareasoning problem is NP-hard, we provide a pseudo-polynomial time optimal solution to the case with known deadlines. We leverage intuitions emerging from this case to provide a fast greedy scheme that significantly improves upon previous schemes even for the case of unknown deadlines. Finally, we show how this new method can be applied inside a practical situated temporal planner. An empirical evaluation suggests that the new planner provides state-of-the-art results on problems where external deadlines play a significant role