Sven Koenig

Bio: Sven Koenig is an academic researcher from University of Southern California. The author has contributed to research in topics: Path (graph theory) & Search algorithm. The author has an hindex of 16, co-authored 40 publications receiving 774 citations.

PRIMAL: Pathfinding via Reinforcement and Imitation Multi-Agent Learning

Abstract: Multi-agent path finding (MAPF) is an essential component of many large-scale, real-world robot deployments, from aerial swarms to warehouse automation. However, despite the community's continued efforts, most state-of-the-art MAPF planners still rely on centralized planning and scale poorly past a few hundred agents. Such planning approaches are maladapted to real-world deployments, where noise and uncertainty often require paths be recomputed online, which is impossible when planning times are in seconds to minutes. We present PRIMAL, a novel framework for MAPF that combines reinforcement and imitation learning to teach fully decentralized policies, where agents reactively plan paths online in a partially observable world while exhibiting implicit coordination. This framework extends our previous work on distributed learning of collaborative policies by introducing demonstrations of an expert MAPF planner during training, as well as careful reward shaping and environment sampling. Once learned, the resulting policy can be copied onto any number of agents and naturally scales to different team sizes and world dimensions. We present results on randomized worlds with up to 1024 agents and compare success rates against state-of-the-art MAPF planners. Finally, we experimentally validate the learned policies in a hybrid simulation of a factory mockup, involving both real world and simulated robots.

Searching with Consistent Prioritization for Multi-Agent Path Finding

Abstract: 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.

Optimal Target Assignment and Path Finding for Teams of Agents

Abstract: We study the TAPF (combined target-assignment and path-finding) problem for teams of agents in known terrain, which generalizes both the anonymous and non-anonymous multi-agent path-finding problems. Each of the teams is given the same number of targets as there are agents in the team. Each agent has to move to exactly one target given to its team such that all targets are visited. The TAPF problem is to first assign agents to targets and then plan collision-free paths for the agents to their targets in a way such that the makespan is minimized. We present the CBM (Conflict-Based Min-Cost-Flow) algorithm, a hierarchical algorithm that solves TAPF instances optimally by combining ideas from anonymous and non-anonymous multi-agent path-finding algorithms. On the low level, CBM uses a min-cost max-flow algorithm on a time-expanded network to assign all agents in a single team to targets and plan their paths. On the high level, CBM uses conflict-based search to resolve collisions among agents in different teams. Theoretically, we prove that CBM is correct, complete and optimal. Experimentally, we show the scalability of CBM to TAPF instances with dozens of teams and hundreds of agents and adapt it to a simulated warehouse system.

Ethical Considerations in Artificial Intelligence Courses

Abstract: The recent surge in interest in ethics in artificial intelligence may leave many educators wondering how to address moral, ethical, and philosophical issues in their AI courses. As instructors we want to develop curriculum that not only prepares students to be artificial intelligence practitioners, but also to understand the moral, ethical, and philosophical impacts that artificial intelligence will have on society. In this article we provide practical case studies and links to resources for use by AI educators. We also provide concrete suggestions on how to integrate AI ethics into a general artificial intelligence course and how to teach a stand-alone artificial intelligence ethics course.

Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks

Abstract: The multi-agent path-finding (MAPF) problem has recently received a lot of attention. However, it does not capture important characteristics of many real-world domains, such as automated warehouses, where agents are constantly engaged with new tasks. In this paper, we therefore study a lifelong version of the MAPF problem, called the multi-agent pickup and delivery (MAPD) problem. In the MAPD problem, agents have to attend to a stream of delivery tasks in an online setting. One agent has to be assigned to each delivery task. This agent has to first move to a given pickup location and then to a given delivery location while avoiding collisions with other agents. We present two decoupled MAPD algorithms, Token Passing (TP) and Token Passing with Task Swaps (TPTS). Theoretically, we show that they solve all well-formed MAPD instances, a realistic subclass of MAPD instances. Experimentally, we compare them against a centralized strawman MAPD algorithm without this guarantee in a simulated warehouse system. TP can easily be extended to a fully distributed MAPD algorithm and is the best choice when real-time computation is of primary concern since it remains efficient for MAPD instances with hundreds of agents and tasks. TPTS requires limited communication among agents and balances well between TP and the centralized MAPD algorithm.

Alone Together: Why We Expect More from Technology and Less from Each Other.

Abstract: The article reviews the book "Alone Together: Why We Expect More From Technology and Less From Each Other," by Sherry Turkle.

Superintelligence: Paths, Dangers, Strategies

Abstract: Hardcover: 352 pagesYear: 2014Publisher: Oxford University PressISBN-13: 978-0199678112“The first ultraintelligent machine is the last invention that man need ever make, provided that the machine i...

The Ethics of AI Ethics -- An Evaluation of Guidelines

Abstract: Current advances in research, development and application of artificial intelligence (AI) systems have yielded a far-reaching discourse on AI ethics. In consequence, a number of ethics guidelines have been released in recent years. These guidelines comprise normative principles and recommendations aimed to harness the "disruptive" potentials of new AI technologies. Designed as a comprehensive evaluation, this paper analyzes and compares these guidelines highlighting overlaps but also omissions. As a result, I give a detailed overview of the field of AI ethics. Finally, I also examine to what extent the respective ethical principles and values are implemented in the practice of research, development and application of AI systems - and how the effectiveness in the demands of AI ethics can be improved.

Trajectory Planning for Quadrotor Swarms

Abstract: We describe a method for multirobot trajectory planning in known, obstacle-rich environments. We demonstrate our approach on a quadrotor swarm navigating in a warehouse setting. Our method consists of following three stages: 1) roadmap generation that generates sparse roadmaps annotated with possible interrobot collisions; 2) discrete planning that finds valid execution schedules in discrete time and space; 3) continuous refinement that creates smooth trajectories. We account for the downwash effect of quadrotors, allowing safe flight in dense formations. We demonstrate computational efficiency in simulation with up to 200 robots and physical plausibility with an experiment on 32 nano-quadrotors. Our approach can compute safe and smooth trajectories for hundreds of quadrotors in dense environments with obstacles in a few minutes.