A general framework for reordering agents asynchronously in distributed CSPs (original) (raw)
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Agile Asynchronous Backtracking for Distributed Constraint Satisfaction Problems
2011
Asynchronous Backtracking is the standard search procedure for distributed constraint reasoning. It requires a total ordering on the agents. All polynomial space algorithms proposed so far to improve Asynchronous Backtracking by reordering agents during search only allow a limited amount of reordering. In this paper, we propose Agile-ABT, a search procedure that is able to change the ordering of agents more than previous approaches. This is done via the original notion of termination value, a vector of stamps labelling the new orders exchanged by agents during search. In Agile-ABT, agents can reorder themselves as much as they want as long as the termination value decreases as the search progresses. Our experiments show the good performance of Agile-ABT when compared to other dynamic reordering techniques.
Corrigendum to “Min-domain retroactive ordering for asynchronous backtracking
Constraints - An International Journal, 2012
The asynchronous backtracking algorithm with dynamic ordering (ABT_DO), proposed in Zivan and Meisels (Constraints 11(2–3):179–197, 2006), allows changing the order of agents during distributed asynchronous complete search. In a later study (Zivan et al., Constraints 14(2):177–198, 2009), retroactive heuristics which allowed more flexibility in the selection of new orders were introduced, resulting in the ABT_DO-Retro algorithm, and a relation between the success of heuristics and the min-domain property was identified. Unfortunately, the description of the time-stampping protocol used to compare orders in ABT_DO-Retro in Zivan et al. (Constraints 14(2):177–198, 2009) is confusing and may lead to an implementation in which ABT_DO-Retro may not terminate. In this corrigendum, we demonstrate the possible undesired outcome and give a detailed and formal description of the correct method for comparing time-stamps in ABT_DO-Retro.
Asynchronous backtracking without adding links: a new member in the ABT family
Artificial Intelligence, 2005
Following the pioneer work of Yokoo and colleagues on the ABT (asynchronous backtracking) algorithm, several ABT-based procedures have been proposed for solving distributed constraint networks. They differ in the way they store nogoods, but they all use additional communication links between unconnected agents to detect obsolete information. In this paper, we propose a new asynchronous backtracking algorithm which does not need to add links between initially unconnected agents. To make the description simpler and to facilitate the comparisons between algorithms, we present a unifying framework from which the new algorithm we propose, as well as existing ones, are derived. We provide an experimental evaluation of these algorithms. * The work of Ismel Brito and Pedro Meseguer is supported by the Spanish project REPLI: TIC-2002-04470-C03.
The message management asynchronous backtracking algorithm
Journal of Experimental & Theoretical Artificial Intelligence, 2008
This paper shows how the Asynchronous Backtracking (Yokoo et al., 1998) algorithm, a well known distributed constraint satisfaction algorithm, produces unnecessary messages and introduces our optimized algorithm, Message Management Asynchronous Backtracking, which reduces the number of messages the agents send. The message management mechanism removes the redundant messages, keeps message queue updated, and handles messages by package instead of individually in order to improve efficiency. Our test results show the algorithm significantly reduces the total number of messages sent and drastically reduces the number of cycles used when solving instances of the graph coloring problem.
2004
In this paper, we develop a formalism called a distributed constraint satisfaction prob/em (distributed CSP) and algorithms for solving distributed CSPs. A distributed CSP is a constraint satisfaction problem in which variables and constraints are distributed among multiple agents. Various application problems in Distributed Artificial Intelligence can be formalized as distributed CSPs. We present our newly developed technique called asynchronous backtracking that allows agents to act asynchronously and concurrently without any global control, while guaranteeing the completeness of the algorithm. Furthermore, we describe how the asynchronous backtracking algorithm can be modified into a more efficient algorithm called an asynchronous weak-commitment search, which can revise a bad decision without exhaustive search by changing the priority order of agents dynamically. The experimental results on various example problems show that the asynchronous weak-commitment search algorithm is, ...
Asynchronous Coordination Under Preferences and Constraints
Structural Information and Communication Complexity, 2016
Adaptive renaming can be viewed as a coordination task involving a set of asynchronous agents, each aiming at grabbing a single resource out of a set of resources totally ordered by their desirability. Similarly, musical chairs is also defined as a coordination task involving a set of asynchronous agents, each aiming at picking one of a set of available resources, where every agent comes with an a priori preference for some resource. We foresee instances in which some combinations of resources are allowed, while others are disallowed. We model these constraints, i.e., the restrictions on the ability to use some combinations of resources, as an undirected graph whose nodes represent the resources, and an edge between two resources indicates that these two resources cannot be used simultaneously. In other words, the sets of resources that are allowed are those which form independent sets in the graph. E.g., renaming and musical chairs are specific cases where the graph is stable (i.e., it the empty graph containing no edges). As for musical chairs, we assume that each agent comes with an a priori preference for some resource. If an agent's preference is not in conflict with the preferences of the other agents, then this preference can be grabbed by the agent. Otherwise, the agents must coordinate to resolve their conflicts, and potentially choose non preferred resources. We investigate the following problem: given a graph, what is the maximum number of agents that can be accommodated subject to non-altruistic behaviors of early arriving agents? We entirely solve this problem under the restriction that agents which cannot grab their preferred resources must then choose a resource among the nodes of a predefined independent set. However, the general case, where agents which cannot grab their preferred resource are then free to choose any resource, is shown to be far more complex. In particular, just for cyclic constraints, the problem is surprisingly difficult. Indeed, we show that, intriguingly, the natural algorithm inspired from optimal solutions to adaptive renaming or musical chairs is sub-optimal for cycles, but proven to be at most 1 to the optimal. The main message of this paper is that finding optimal solutions to the coordination with constraints and preferences task requires to design "dynamic" algorithms, that is, algorithms of a completely different nature than the "static" algorithms used for, e.g., renaming.
The Distributed Constraint Satisfaction Problem: Formalization and Algorithms
IEEE Transactions on Knowledge and Data Engineering, 1998
We develop a formalism called a distributed constraint satisfaction problem (distributed CSP) and algorithms for solving distributed CSPs. A distributed CSP is a constraint satisfaction problem in which variables and constraints are distributed among multiple agents. Various application problems in distributed artificial intelligence can be formalized as distributed CSPs. We present our newly developed technique called asynchronous backtracking that allows agents to act asynchronously and concurrently without any global control, while guaranteeing the completeness of the algorithm. Furthermore, we describe how the asynchronous backtracking algorithm can be modified into a more efficient algorithm called an asynchronous weak-commitment search, which can revise a bad decision without exhaustive search by changing the priority order of agents dynamically. The experimental results on various example problems show that the asynchronous weak-commitment search algorithm is, by far more, efficient than the asynchronous backtracking algorithm and can solve fairly large-scale problems
The Asynchronous Backtracking Family
2003
In the last years, the AI community has shown an increasing interest in distributed problem solving. In the scope of distributed constraint reasoning, several asynchronous backtracking procedures have been proposed for nding solutions in a constraint network distributed among several computers. They dier in the way they store failing combinations of values (nogoods), and in the way they check the
Nogood-based asynchronous forward checking algorithms
Constraints - An International Journal, 2013
We propose two new algorithms for solving Distributed Constraint Satisfaction Problems (DisCSPs). The first algorithm, AFC-ng, is a nogood-based version of Asynchronous Forward Checking (AFC). Besides its use of nogoods as justification of value removals, AFC-ng allows simultaneous backtracks going from different agents to different destinations. The second algorithm, Asynchronous Forward Checking Tree (AFC-tree), is based on the AFC-ng algorithm and is performed on a pseudo-tree ordering of the constraint graph. AFC-tree runs simultaneous search processes in disjoint problem subtrees and exploits the parallelism inherent in the problem. We prove that AFC-ng and AFC-tree only need polynomial space. We compare the performance of these algorithms with other DisCSP algorithms on random DisCSPs and instances from real benchmarks: sensor networks and distributed meeting scheduling. Our experiments show that AFC-ng improves on AFC and that AFC-tree outperforms all compared algorithms, particularly on sparse problems.
ADOPT-ing: unifying asynchronous distributed optimization with asynchronous backtracking
Autonomous Agents and Multi-Agent Systems, 2008
This article presents an asynchronous algorithm for solving Distributed Constraint Optimization problems (DCOPs). The proposed technique unifies asynchronous backtracking (ABT) and asynchronous distributed optimization (ADOPT) where valued nogoods enable more flexible reasoning and more opportunities for communication, leading to an important speed-up. While feedback can be sent in ADOPT by COST messages only to one predefined predecessor, our extension allows for sending such information to any relevant agent. The concept of valued nogood is an extension by Dago and Verfaille of the concept of classic nogood that associates the list of conflicting assignments with a cost and, optionally, with a set of references to culprit constraints. DCOPs have been shown to have very elegant distributed solutions, such as ADOPT, distributed asynchronous overlay (DisAO), or DPOP. These algorithms are typically tuned to minimize the longest causal chain of messages as a measure of how the algorithms will scale for systems with remote agents (with large latency in communication). ADOPT has the property of maintaining the initial distribution of the problem. To be efficient, ADOPT needs a preprocessing step consisting of computing a Depth-First Search (DFS) tree on the constraint graph. Valued nogoods allow for automatically detecting and exploiting the best DFS tree compatible with the current ordering. To exploit such DFS trees it is now sufficient to ensure that they exist. Also, the inference rules available for valued nogoods help to exploit schemes of communication where more feedback is sent to higher priority agents. Together they result in an order of magnitude improvement. 1. DCOP definitions could also include it to help specify branch and bound solvers. 10. Because the corresponding constraint increases for the first time the cost of the computed nogood. 11. Assuming no mechanism is used to block immediate retransmission of nogoods, such as our lastSent structure. 12. Assignments having the same value are considered identical, even if their tag differs (allowing for re-using old nogoods). 13. Note that with the first scheme (i), where assignments are not tagged with counters, ADOPTing should not delete old nogoods from lr (which is done with the second scheme), but checks them when ok? messages are received.