In the multi agent path finding problem (MAPF) paths should be found for several agents, each with a different start and goal position such that agents do not collide. Previous optimal solvers applied global A*-based searches. We present a new search algorithm called Conflict Based Search (CBS). CBS is a two-level algorithm. At the high level, a search is performed on a tree based on conflicts between agents. At the low level, a search is performed only for a single agent at a time. In many cases this reformulation enables CBS to examine fewer states than A* while still maintaining optimality. We analyze CBS and show its benefits and drawbacks. Experimental results on various problems shows a speedup of up to a full order of magnitude over previous approaches.

Conflict-based search for optimal multi-agent path finding

https://www.aaai.org/ocs/index.php/AAAI/AAAI12/paper/viewFile/5062/5239

Conflict-based search for optimal multi-agent pathfinding

Bitcoin is an innovative decentralized cryptocurrency whose core security relies on a "proof of work" procedure, which requires network participants to repeatedly compute hashes on inputs from a large search space. Finding one of the rare inputs that generates an extremely low hash value is considered a successful attempt, allowing miners to approve new transactions and, in return, to collect rewards in bitcoins.This reward allocation, which provides the incentive for miners to participate, is a random process with a large variance. Miners who desire a steady income thus often participate in mining pools that divide among their members the earned rewards, and reduce this variance. Mining pools are slightly better at coordinating participants due to lower-latency communication, a fact which implies that they manage to collect slightly higher rewards.We examine dynamics of pooled mining and the rewards that pools manage to collect, and use cooperative game theoretic tools to analyze how pool members may share these rewards. We show that for some network parameters, especially under high transaction loads, it is difficult or even impossible to distribute rewards in a stable way: some participants are always incentivized to switch between pools.

/pdf/bitcoin-mining-pools-a-cooperative-game-theoretic-analysis-4vki8e97oq.pdf

Bitcoin Mining Pools: A Cooperative Game Theoretic Analysis

We describe an intelligent personal assistant that has been developed to aid a busy knowledge worker in managing time commitments and performing tasks. The design of the system was motivated by the complementary objectives of (1) relieving the user of routine tasks, thus allowing her to focus on tasks that critically require human problem-solving skills, and (2) intervening in situations where cognitive overload leads to oversights or mistakes by the user. The system draws on a diverse set of AI technologies that are linked within a Belief-Desire-Intention (BDI) agent system. Although the system provides a number of automated functions, the overall framework is highly user centric in its support for human needs, responsiveness to human inputs, and adaptivity to user working style and preferences.

/pdf/an-intelligent-personal-assistant-for-task-and-time-2a411f86xg.pdf

An Intelligent Personal Assistant for Task and Time Management

Distributed constraint optimization (DCOP) problems are a popular way of formulating and solving agent-coordination problems. It is often desirable to solve DCOP problems optimally with memory-bounded and asynchronous algorithms. We introduce Branch-and-Bound ADOPT (BnB-ADOPT), a memory-bounded asynchronous DCOP algorithm that uses the message passing and communication framework of ADOPT, a well known memory-bounded asynchronous DCOP algorithm, but changes the search strategy of ADOPT from best-first search to depth-first branch-and-bound search. Our experimental results show that BnB-ADOPT is up to one order of magnitude faster than ADOPT on a variety of large DCOP problems and faster than NCBB, a memory-bounded synchronous DCOP algorithm, on most of these DCOP problems.

/pdf/bnb-adopt-an-asynchronous-branch-and-bound-dcop-algorithm-151l4qvhjm.pdf

BnB-ADOPT: an asynchronous branch-and-bound DCOP algorithm

Extreme teams, large-scale agent teams operating in dynamic environments, are on the horizon. Such environments are problematic for current task allocation algorithms due to the lack of locality in agent interactions. We propose a novel distributed task allocation algorithm for extreme teams, called LA-DCOP, that incorporates three key ideas. First, LA-DCOP's task allocation is based on a dynamically computed minimum capability threshold which uses approximate knowledge of overall task load. Second, LA-DCOP uses tokens to represent tasks and further minimize communication. Third, it creates potential tokens to deal with inter-task constraints of simultaneous execution. We show that LA-DCOP convincingly outperforms competing distributed task allocation algorithms while using orders of magnitude fewer messages, allowing a dramatic scale-up in extreme teams, upto a fully distributed, proxybased team of 200 agents. Varying threshold are seen as a key to outperforming competing distributed algorithms in the domain of simulated disaster rescue.

/pdf/allocating-tasks-in-extreme-teams-3rgxdbn4ca.pdf

Allocating tasks in extreme teams

Efficient coordination among large numbers of heterogeneous agents promises to revolutionize the way in which some complex tasks, such as responding to urban disasters can be performed. However, state of the art coordination algorithms are not capable of achieving efficient and effective coordination when a team is very large. Building on recent successful token-based algorithms for task allocation and information sharing, we have developed an integrated and efficient approach to effective coordination of large scale teams. We use tokens to encapsulate anything that needs to be shared by the team, including information, tasks and resources. The tokens are efficiently routed through the team via the use of local decision theoretic models. Each token is used to improve the routing of other tokens leading to a dramatic performance improvement when the algorithms work together. We present results from an implementation of this approach which demonstrates its ability to coordinate large teams.

/pdf/an-integrated-token-based-algorithm-for-scalable-oz466mxnhc.pdf

An integrated token-based algorithm for scalable coordination

http://www.bgu.ac.il/~zivanr/files/ALS_AIJ.pdf

Explorative anytime local search for distributed constraint optimization

Coordinating a mobile sensor team (MST) to cover targets is a challenging problem in many multiagent applications. Such applications are inherently dynamic due to changes in the environment, technology failures, and incomplete knowledge of the agents. Agents must adaptively respond by changing their locations to continually optimize the coverage of targets. We propose distributed constraint optimization problems (DCOP)_MST, a new model for representing MST problems that is based on DCOP. In DCOP_MST, agents maintain variables for their physical positions, while each target is represented by a constraint that reflects the quality of coverage of that target. In contrast to conventional, static DCOPs, DCOP_MST not only permits dynamism but exploits it by restricting variable domains to nearby locations; consequently, variable domains and constraints change as the agents move through the environment. DCOP_MST confers three major advantages. It directly represents the multiple forms of dynamism inherent in MSTs. It also provides a compact representation that can be solved efficiently with local search algorithms, with information and communication locality based on physical locality as typically occurs in MST applications. Finally, DCOP_MST facilitates organization of the team into multiple sub-teams that can specialize in different roles and coordinate their activity through dynamic events. We demonstrate how a search-and-detection team responsible for finding new targets and a surveillance sub-team tasked with coverage of known targets can effectively work together to improve performance while using the DCOP_MST framework to coordinate. We propose different algorithms to meet the specific needs of each sub-team and several methods for cooperation between sub-teams. For the search-and-detection team, we develop an algorithm based on the DSA that forces intensive exploration for new targets. For the surveillance sub-team, we adapt several incomplete DCOP algorithms, including MGM, DSA, DBA, and Max-sum, which requires us to develop an efficient method for agents to find the value assignment in their local environment that is optimal in minimizing the maximum unmet coverage requirement over all targets. The disadvantage of dynamic domains based on physical locality is that adaptations of standard local search algorithms tend to become trapped in local optima where targets beyond the immediate range of the agents go uncovered. To address this shortcoming we develop exploration methods to be used with the local search algorithms. Our algorithms are extensively evaluated in a simulation environment. We use a reputation model to determine the individual credibility of agents and consider both additive and submodular joint credibility functions for determining coverage of targets by multiple agents. The performance is measured on two objectives: minimizing the maximum remaining coverage requirement, and minimizing the sum of remaining coverage requirements. Our results show that DSA and MGM with the exploration heuristics outperform the other incomplete algorithms across a wide range of settings. Furthermore, organizing the team into two sub-teams leads to significant gains in performance, and performance continues to improve with greater cooperation between the sub-teams.

Distributed constraint optimization for teams of mobile sensing agents

Realistic multi-agent team applications often feature dynamic environments with soft deadlines that penalize late execution of tasks. This puts a premium on quickly allocating tasks to agents, but finding the optimal allocation is NP-hard because tasks must be executed sequentially by agents. We propose a novel task allocation algorithm that finds allocations that are fair (envy-free), balancing the load and sharing important tasks between agents, and efficient (Pareto optimal) by using a Fisher market based on a simplified problem model. Such allocations can be easily sequenced to yield high quality solutions, as shown empirically on problems inspired by real police logs.

/pdf/dynamic-multi-agent-task-allocation-with-spatial-and-2sl9evy2tb.pdf

Steven Okamoto

Papers

Allocating tasks in extreme teams

An integrated token-based algorithm for scalable coordination

Explorative anytime local search for distributed constraint optimization

Distributed constraint optimization for teams of mobile sensing agents

Dynamic multi-agent task allocation with spatial and temporal constraints