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AAAI Conferences


Twenty-First International Joint Conference on Artificial Intelligence

AAAI Conferences

The theme of IJCAI-09 is "The Interdisciplinary Reach of Artificial Intelligence," with a focus on the broad impact of artificial intelligence on science, engineering, medicine, social sciences, arts, and humanities. The conference will include invited talks, workshops, tutorials, and other events dedicated to this theme.


Twenty-Third IAAI Conference

AAAI Conferences

The Twenty-Third Innovative Applications of Artificial Intelligence Conference (IAAI-11) will be held in San Francisco, California at the Hyatt Regency San Francisco, from August 9–11, 2011, USA. The proceedings will be published by AAAI Press. This site only contains the published proceedings of the conference. For information about the conference in general, please see the conference website.


Twenty-Fourth IAAI Conference

AAAI Conferences

The Twenty-Fourth Innovative Applications of Artificial Intelligence conference (IAAI 2012) will be held in Toronto, Ontario, Canada, July 22–26 2012. The proceedings will be published by AAAI Press. This site only contains the published proceedings of the conference. For information about the conference in general, please see the conference website.


On SAT-Based Approaches for Multi-Agent Path Finding with the Sum-of-Costs Objective

AAAI Conferences

Multi-agent path finding (MAPF) deals with the problem of finding collision-free paths for a set of agents. Each agent moves from its start location to its destination location in a shared environment represented by a graph. Reduction-based solving approaches for MAPF, for example reduction to SAT, exploit a time-expended layered graph, where each layer corresponds to specific time. Hence, these approaches are natural for minimizing makespan (the shortest time till all agents reach their destinations). Modeling the other frequently used objective, namely Sum of Costs (SOC; sum of paths lengths of all agents) is more difficult as the solution with the smallest SOC may not be reached in the time-expended graph with the smallest makespan. In this paper we suggest two novel approaches to estimate the makespan, that guarantees existence of a SOC-optimal solution. The approaches are empirically compared with an existing reduction-based method as well as with the state-of-the-art search-based optimal MAPF solver.


A Learning-Based Framework for Memory-Bounded Heuristic Search: First Results

AAAI Conferences

Many existing boundedly-suboptimal heuristic search algorithms are variants of best-first search. Due to memory limitations, these algorithms are unable to solve problems with extremely large search spaces. In this paper, we present a framework that allows best-first search algorithms to solve problems with such large search spaces given a (reasonable) memory bound while also preserving optimality guarantees in tree-structured search spaces. In our framework, a given algorithm is run several times. In each search episode, the algorithm expands up to a user-defined number of states. After each episode, unless the goal has been found, the heuristic values of the generated states are updated using a linear-time algorithm that preserves consistency in tree-structured search spaces. In subsequent search episodes, only the heuristic values of the states generated in the previous episode need to be kept in memory. We present experimental results where we plug A*, GBFS, and wA* into our framework to solve traveling salesman problems and compare them against benchmark linear-memory algorithms like DFBnB and wDFBnB.


Enriching Non-Parametric Bidirectional Search Algorithms — Extended Abstract

AAAI Conferences

NBS is a non-parametric bidirectional search algorithm, proved to expand at most twice the number of node expansions required to verify the optimality of a solution. We introduce new variants of NBS that are aimed at finding all optimal solutions. We then introduce an algorithmic framework that includes NBS as a special case. Finally, we introduce DVCBS, a new algorithm in this framework that aims to further reduce the number of expansions. Unlike NBS, DVCBS does not have any worst-case bound guarantees, but in practice it outperforms NBS in verifying the optimality of solutions.


Zero-Aware Pattern Databases with 1-Bit Compression for Sliding Tile Puzzles

AAAI Conferences

A pattern database (PDB) is a pre-computed lookup table storing shortest distances from abstract states to abstract goal states. PDBs are key components in heuristic search as their entries are used to prune paths that cannot lead to an optimal solution. With the sliding-tile puzzle as an exemplary application domain, we present methods to improve the precision and size of PDBs by improving additive pattern databases to zero-aware additive pattern databases (ZPDBs), reducing the compression rate from previously 1.6 bit to 1 bit per entry, generating optimal additive pattern partitionings, and building effective collections of pattern databases. With these enhancements, we achieve an overall 8.59-fold performance gain on the 24-puzzle compared to the previously best set of 6-tile PDBs.


Extended Abstract: Lifelong Path Planning with Kinematic Constraints for Multi-Agent Pickup and Delivery

AAAI Conferences

The Multi-Agent Pickup and Delivery (MAPD) problem models applications where a large number of agents attend to a stream of incoming pickup-and-delivery tasks. Token Passing (TP) is a recent MAPD algorithm that is efficient and effective. We make TP even more efficient and effective by using a novel combinatorial search algorithm, called Safe Interval Path Planning with Reservation Table (SIPPwRT), for single-agent path planning. SIPPwRT uses an advanced data structure that allows for fast updates and lookups of the current paths of all agents in an online setting. The resulting MAPD algorithm TP-SIPPwRT takes kinematic constraints of real robots into account directly during planning, computes continuous agent movements with given velocities that work on non-holonomic robots rather than discrete agent movements with uniform velocity, and is complete for well-formed MAPD instances. We demonstrate its benefits for automated warehouses using both an agent simulator and a standard robot simulator. For example, we demonstrate that it can compute paths for hundreds of agents and thousands of tasks in seconds and is more efficient and effective than existing MAPD algorithms that use a post-processing step to adapt their paths to continuous agent movements with given velocities. This paper was published at AAAI 2019.


Improving Bidirectional Heuristic Search by Bounds Propagation

AAAI Conferences

Recent work in bidirectional heuristic search characterize pairs of nodes from which at least one node must be expanded in order to ensure optimality of solutions. We use these findings to propose a method for improving existing heuristics by propagating lower bounds between the forward and backward frontiers. We then define a number of desirable properties for bidirectional heuristic search algorithms, and show that applying the bound propagations adds these properties to many existing algorithms (e.g. to the MM family of algorithms). Finally, experimental results show that applying these propagations significantly reduce the running time of various algorithms.


Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks

AAAI Conferences

The multi-agent pathfinding problem (MAPF) is the fundamental problem of planning paths for multiple agents, where the key constraint is that the agents will be able to follow these paths concurrently without colliding with each other. Applications of MAPF include automated warehouses, autonomous vehicles, and robotics. Research on MAPF has been flourishing in the past couple of years. Different MAPF research papers assume different sets of assumptions, e.g., whether agents can traverse the same road at the same time, and have different objective functions, e.g., minimize makespan or sum of agents' actions costs. These assumptions and objectives are sometimes implicitly assumed or described informally. This makes it difficult for establishing appropriate baselines for comparison in research papers, as well as making it difficult for practitioners to find the papers relevant to their concrete application. This paper aims to fill this gap and facilitate future research and practitioners by providing a unifying terminology for describing the common MAPF assumptions and objectives. In addition, we also provide pointers to two MAPF benchmarks. In particular, we introduce a new grid-based benchmark for MAPF, and demonstrate experimentally that it poses a challenge to contemporary MAPF algorithms.