Asynchronous Backtracking Algorithm Research Articles

The Dynamic Broker Dominant Supply-Demand Matching Decision Using Multiagent System

Supply-demand matching is critical for the increase of response speed, the improvement of resource utilization, and the building of long-term partnership. This study will contribute to the present supply-demand matching studies by (1) summarizing the dynamic broker dominant supply-demand matching problem (B-SDMP), (2) proposing the dynamic broker dominant supply-demand matching approach, inclusive of (a) the building of the distributed constraint satisfaction model based on the dynamic negotiation among agents and (b) the design of an asynchronous backtracking algorithm according to interaction among agents, and (3) the design and development of a multiagent dynamic supply-demand matching system. To verify the validity and usability of the method, the system test and case simulation are conducted. The matching solutions yielded from this B-SDMP analysis can help the buyers find the appropriate sellers on one commodity/service under dynamic environment and stimulate the building of long-term partnership among the sellers, buyers, and broker as a stable supply chain.

Corrigendum to “Min-domain retroactive ordering for asynchronous backtracking”

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.

The power of ants in solving Distributed Constraint Satisfaction Problems

Distributed Constraint Satisfaction (DCSP) has long been considered an important area of research for artificial intelligence and multi-agent systems. Also, Ant Colony Optimization (ACO) is an important evolutionary method for solving various optimization problems. This paper demonstrates the power of ants in solving DCSPs and describes a new approach for such a solution, showing how it differs from previous ACO-based DCSP solvers. The presented algorithm is designed to provide the special requirements that are important in the distributed form of Constraint Satisfaction Problem (CSP). The paper describes the important criteria for distributed CSP and then demonstrates how the presented algorithm stands out over similar DCSP solvers considering these criteria. Finally, the proposed approach is evaluated on random binary problems. The practical results show that this method, in most of the cases, outperforms the Asynchronous Backtracking Algorithm (ABT) and Distributed Breakout Algorithm (DBA) two important algorithms in this field of research.

Cooperative Problem Solving against Adversary: Quantified Distributed Constraint Satisfaction Problem

In this paper, we extend the traditional formalization of distributed constraint satisfaction problems (DisCSP) to a quantified DisCSP. A quantified DisCSP includes several universally quantified variables, while all of the variables in a traditional DisCSP are existentially quantified. A universally quantified variable represents a choice of nature or an adversary. A quantified DisCSP formalizes a situation where a team of agents is trying to make a robust plan against nature or an adversary. In this paper, we present the formalization of such a quantified DisCSP and develop an algorithm for solving it by generalizing the asynchronous backtracking algorithm used for solving a DisCSP. In this algorithm, agents communicate a value assignment called a good in addition to the nogood used in asynchronous backtracking. Interestingly, the procedures executed by an adversarial/cooperative agent for good/nogood are completely symmetrical. Furthermore, we develop a method that improves this basic algorithm. Experimental evaluation results illustrate that we observe an easy-hard-easy transition by changing the tightness of the constraints, while very loose problem instances are relatively hard. The modification of the basic algorithm is also effective and reduces the number of cycles by approximately 25% for the hardest problem instances.

Distributed SAT

We present DPLL ABT, a distributed Satisfiability solver (SAT) (Ansotegui and Manya in IberoAm J Artif Intell 7(20):43---56, 2003) designed to solve distributed SAT problem instances. Since SAT is a particular case of constraint satisfaction, we propose a solving method based on the Asynchronous Backtracking algorithm (ABT) (Yokoo et al. in IEEE Trans Knowl Data Eng 10(5):673---685, 1998) developed for distributed constraint reasoning. In addition, we have applied the Davis-Putnam procedure (DPLL) in every agent, plus the minimum conflict heuristic in case DPLL does not detect any inconsistency. The resulting algorithm improves the performance in terms of communication cost and computational effort versus the basic ABT. The SAT instance is distributed into agents, which cooperate to solve SAT instances just sharing the minimum information. We also present the experimental results that demonstrate the performance of the method in terms of communication and execution time comparing the performance with the basic ABT algorithm.

The message management asynchronous backtracking algorithm

In this paper we show how the asynchronous backtracking algorithm, a well-known distributed constraint satisfaction algorithm, produces unnecessary messages and introduce 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 the message queue updated, and handles messages by package instead of individually in order to improve efficiency. Our test results shows that the algorithm significantly reduces the total number of messages sent and drastically reduces the number of cycles used when solving instances of the graph colouring problem.

Distributed constraint satisfaction with partially known constraints

Distributed constraint satisfaction problems (DisCSPs) are composed of agents connected by constraints. The standard model for DisCSP search algorithms uses messages containing assignments of agents. It assumes that constraints are checked by one of the two agents involved in a binary constraint, hence the constraint is fully known to both agents. This paper presents a new DisCSP model in which constraints are kept private and are only partially known to agents. In addition, value assignments can also be kept private to agents and not be circulated in messages. Two versions of a new asynchronous backtracking algorithm that work with partially known constraints (PKC) are presented. One is a two-phase asynchronous backtracking algorithm and the other uses only a single phase. Another new algorithm preserves the privacy of assignments by performing distributed forward-checking (DisFC). We propose to use entropy as quantitative measure for privacy. An extensive experimental evaluation demonstrates a trade-off between preserving privacy and the efficiency of search, among the different algorithms.

Dynamic Ordering for Asynchronous Backtracking on DisCSPs

An algorithm that performs asynchronous backtracking on distributed $$CSPs$$ , with dynamic ordering of agents is proposed, $$ABT\_DO$$ . Agents propose reorderings of lower priority agents and send these proposals whenever they send assignment messages. Changes of ordering triggers a different computation of $$Nogoods$$ . The dynamic ordered asynchronous backtracking algorithm uses polynomial space, similarly to standard $$ABT$$ . The $$ABT\_DO$$ algorithm with three different ordering heuristics is compared to standard $$ABT$$ on randomly generated $$DisCSPs$$ . A Nogood-triggered heuristic, inspired by dynamic backtracking, is found to outperform static order $$ABT$$ by a large factor in run-time and improve the network load.

Asynchronous backtracking without adding links: a new member in the ABT family

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 distributed constraint satisfaction problem: formalization and algorithms

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.