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.
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.
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.
Ma, Hang (University of Southern California) | Harabor, Daniel (Monash University) | Stuckey, Peter J. (Monash University) | Li, Jiaoyang (University of Southern California) | Koenig, Sven (University of Southern California)
We study prioritized planning for Multi-Agent Path Finding (MAPF). Existing prioritized MAPF algorithms depend on rule-of-thumb heuristics and random assignment to determine a fixed total priority ordering of all agents a priori. We instead explore the space of all possible partial priority orderings as part of a novel systematic and conflict-driven combinatorial search framework. In a variety of empirical comparisons, we demonstrate state-of-the-art solution qualities and success rates, often with similar runtimes to existing algorithms. We also develop new theoretical results that explore the limitations of prioritized planning, in terms of completeness and optimality, for the first time. This paper was published at AAAI 2019.
Stern, Roni (Ben-Gurion University of the Negev) | Sturtevant, Nathan R. (University of Alberta) | Felner, Ariel (Ben-Gurion University of the Negev) | Koenig, Sven (University of Southern California) | Ma, Hang (University of Southern California) | Walker, Thayne T. (University of Denver) | Li, Jiaoyang (University of Southern California) | Atzmon, Dor (Ben Gurion University of the Negev) | Cohen, Liron (University of Southern California) | Kumar, T. K. Satish (University of Southern California) | Barták, Roman (Charles University) | Boyarski, Eli (Ben Gurion University of the Negev)
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.
We present a novel search scheme for privacy-preserving multi-agent planning. Inspired by UCT search, the scheme is based on growing an asynchronous search tree by running repeated trials through the tree. We describe key differences to classical multi-agent forward search, discuss theoretical properties of the presented approach, and evaluate it based on benchmarks from the CoDMAP competition. As a secondary contribution, we describe a technique that extends the regular search approach by small explorative trials which are performed subsequent to each node expansion. We show that this technique significantly increases the number of problems solved for all algorithms considered, including MAFS.
Multi-agent pathfinding is the problem of finding a non-interfering paths for a set of agents, such that if the agents follow these paths then each agent will reach its desired destination. Recent years have shown tremendous advances in this field, with optimal and suboptimal algorithms that are able to plan paths for over 100 agents in reasonable time. However, autonomous mobile agents are prime targets for cyber-security attacks, where an adversary may take control over an agent to disrupt the agents execution of their plan. This threat raises two questions. The first question is how much damage can an agent do if it does not follow its plan. The second question is how can one plan a-priori to be as robust as possible to such cyber-attacks. In this work, We provide an answer to both questions. To compute the maximal amount of damage that an adversary agent can do, we define a corresponding graph search problem and solve this problem with A*. Then, we provide a very simple method to choose a solution that is robust to such damages. We demonstrate both algorithms in simulation over standard multi-agent pathfinding domains.
In this paper, we study a problem from the realm of multi-criteria decision making in which the goal is to select from a given set S of d-dimensional objects a minimum sized subset S' with bounded regret. Thereby, regret measures the unhappiness of users which would like to select their favorite object from set S but now can only select their favorite object from the subset S'. Previous work focused on bounding the maximum regret which is determined by the most unhappy user. We propose to consider the average regret instead which is determined by the sum of (un)happiness of all possible users. We show that this regret measure comes with desirable properties as supermodularity which allows to construct approximation algorithms. Furthermore, we introduce the regret minimizing permutation problem and discuss extensions of our algorithms to the recently proposed k-regret measure. Our theoretical results are accompanied with experiments on a variety of inputs with d up to 7.
Li, Jiaoyang (University of Southern California) | Boyarski, Eli (Ben-Gurion University of the Negev) | Felner, Ariel (Ben-Gurion University of the Negev) | Ma, Hang (University of Southern California) | Koenig, Sven (University of Southern California)
Conflict-Based Search (CBS) and its enhancements are among the strongest algorithms for Multi-Agent Pathfinding. Recent work introduced an admissible heuristic to guide the high-level search of CBS. In this work, we prove the limitation of this heuristic, as it is based on cardinal conflicts only. We then introduce two new admissible heuristics by reasoning about the pairwise dependency between agents. Empirically, CBS with both new heuristics significantly improves the success rate over CBS with the recent heuristic and reduces the number of expanded nodes and runtime by up to a factor of 50, yielding a new state-of-the-art CBS-based algorithm.
Nguyen, Van (New Mexico State University) | Obermeier, Philipp (University of Postdam) | Son, Tran Cao (New Mexico State University) | Schaub, Torsten (Washington University in St. Louis) | Yeoh, William (Washington University in St. Louis)
In Multi-Agent Path Finding (MAPF), a team of agents needs to find collision-free paths from their starting locations to their respective targets. Combined Target Assignment and Path Finding (TAPF) extends MAPF by including the problem of assigning targets to agents as a precursor to the MAPF problem. A limitation of both models is their assumption that the number of agents and targets are equal, which is invalid in some applications. We address this limitation by generalizing TAPF to allow for (1) unequal number of agents and tasks; (2) tasks to have deadlines by which they must be completed; (3) ordering of groups of tasks to be completed; and (4) tasks that are composed of a sequence of checkpoints that must be visited in a specific order. Further, we model the problem using answer set programming (ASP) to show that customizing the desired variant of the problem is simple -- one only needs to choose the appropriate combination of ASP rules to enforce it. We also demonstrate experimentally that if problem specific information can be incorporated into the ASP encoding then ASP based methods can be efficient and can scale up to solve practical applications.