Inter-agent Constraints Research Articles

Formation Control of Cooperative-Competitive Robot Manipulators with Inter-agent Constraints

This paper addresses the distributed formation control problem of networked robot manipulators in end-effector coordinates. We propose a distributed bipartite formation controller that guarantees collision avoidance and maximum distance maintenance for cooperative and competitive manipulators’ end-effectors. In the considered setting, two groups of manipulators are formed and reach bipartite consensus. On the other hand, the end-effectors in the same group achieve formation. We design a gradient-based control law using barrier-Lyapunov functions to ensure that the constraints on the end-effectors are satisfied. Moreover, we establish asymptotic stability of the bipartite formation manifold. Finally, we illustrate our theoretical results via numerical simulations.

Robust Formation Control of Robot Manipulators With Inter-Agent Constraints Over Undirected Signed Networks

Robust Formation Control of Robot Manipulators With Inter-Agent Constraints Over Undirected Signed Networks

Asynchronous Backtracking variant applied to a rolls replacement process within a wire rod mill

There is a wide number of application problems included in multi-agent systems and distributed artificial intelligence which can be described as the issue of finding a consistent combination satisfying all the existing inter-agent constraints, that is, they are distributed constraint satisfaction problems (DisCSPs). In this paper, a new technique for solving such kind of issues when they can be modelled as a succession of interrelated elements with constraints among an agent and its immediately following, having one of the agents the ability to notify either that the problem has been solved or that there are not any possible solutions, is presented. It has been developed using Asynchronous Backtracking, one of the most known algorithms for solving DisCSPs as a basis. To validate its applicability, it has been tested in a wire rod rolling mill for rolls replacement decision making process. It was implemented using the PADE multi-agent framework. The results demonstrate the method is valid to solve this kind of problems usually solved with recursive algorithms such as backtracking.

Towards addressing dynamic multi-agent task allocation in law enforcement

Police officers conduct routine patrols and perform tasks in response to reported incidents. The importance of each task varies from low (e.g. noise complaint) to high (e.g. murder). The workload associated with each task, indicating the amount of work to be completed for the incident to be processed, may vary as well. Multiple officers with heterogeneous skills may work together on important tasks to share the workload and improve response time. To deal with the underlying law enforcement problem (LEPH), one needs to allocate police officers to dynamic tasks whose locations, arrival times, and importance levels are unknown a priori. Addressing this challenge and inspired by real police logs, this research aims to solve the LEPH problem by using and comparing three methods: Fisher market-based FMC_TAH+, swarm intelligence HDBA, and Simulated Annealing SA algorithms. FMC_TAH+ is implemented, using agents as buyers and tasks as goods, to compute fair allocations (i.e. envy-free), and efficient (i.e. Pareto-optimal) in a polynomial or pseudo-polynomial time. FMC_TAH+ allocations are heuristically scheduled, considering inter-agent constraints on shared tasks. HDBA, a probabilistic swarm intelligence algorithm inspired by the emergent behavior of social bees, was previously implemented to allocate agents to tasks based on agent performance, task priorities, and distances between agents and task-execution locations. SA is a meta-heuristic for approximating the global optimums in large optimization problems. The three methods were compared in this study for five different performance measures that are commonly used by law enforcement authorities. The results indicate an advantage for FMC_TAH+ both in total utility and in the average arrival time to tasks. Also, compared respectively to HDBA and SA, FMC_TAH+ leads to 34% and 32% higher team utility in the highest shift workload.

Market Clearing–based Dynamic Multi-agent Task Allocation

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. However, when such problems include temporal and spatial constraints that require tasks to be executed sequentially by agents, they are NP-hard, and thus are commonly solved using general and specifically designed incomplete heuristic algorithms. We propose FMC_TA, a novel such incomplete task allocation algorithm that allows tasks to be easily sequenced to yield high-quality solutions. FMC_TA first finds allocations that are fair (envy-free), balancing the load and sharing important tasks among agents, and efficient (Pareto optimal) in a simplified version of the problem. It computes such allocations in polynomial or pseudo-polynomial time (centrally or distributedly, respectively) using a Fisher market with agents as buyers and tasks as goods. It then heuristically schedules the allocations, taking into account inter-agent constraints on shared tasks. We empirically compare our algorithm to state-of-the-art incomplete methods, both centralized and distributed, on law enforcement problems inspired by real police logs. We present a novel formalization of the law enforcement problem, which we use to perform our empirical study. The results show a clear advantage for FMC_TA in total utility and in measures in which law enforcement authorities measure their own performance. Besides problems with realistic properties, the algorithms were compared on synthetic problems in which we increased the size of different elements of the problem to investigate the algorithm’s behavior when the problem scales. The domination of the proposed algorithm was found to be consistent.

An agent-based fuzzy constraint-directed negotiation model for solving supply chain planning and scheduling problems

Supply chain planning and scheduling problems require manufacturers (project managers) to determine product configurations, select suppliers (contractors) for task allocation, and schedule project tasks while considering various production constraints among manufacturers and suppliers. Most relevant studies have focused on finding an optimal solution based on complete information provided by each enterprise in a supply chain. However, the practical implementation of complete information sharing is difficult, if not impossible, because of the fully distributed nature of the supply chain process. This work proposes an agent-based fuzzy constraint-directed negotiation (AFCN) model to solve problems associated with supply chain planning and scheduling, which are modeled as a set of fuzzy constraint satisfaction problems (FCSPs) that are interlinked via inter-agent constraints. To accommodate the perspectives and interests of each enterprise in a supply chain, conflicts among FCSPs are resolved using the AFCN protocol through the iterative exchange of offers/counter-offers with limited information sharing and without privacy breaches. A proposed offer/counter-offer represents not only a set of acceptable solutions and preferences for an operational task but also the possibility of conflict in this area. For each FCSP, the incremental process of offer/counter-offer evaluation eliminates redundant and infeasible solutions. The sharing of limited non-strategic sensitive information among agents enables them to elucidate their opponents’ intentions through iterative negotiations, such that the agents can reach an agreement while ensuring that the solution to a project planning and scheduling problem is satisfactory. The AFCN model is also sufficiently flexible to incorporate different negotiation strategies such as competitive, win-win, and collaborative strategies, for various production environments. Herein, a numerical study was conducted to examine the practical viability and effectiveness of the proposed AFCN model. The experimental results show that the proposed AFCN model not only can generate a schedule that is comparable to a near-optimal solution but also is time-efficient. This indicates that the proposed AFCN is a practical and effective method for solving supply chain planning and scheduling problems in fully distributed environments.

Agent-based fuzzy constraint-directed negotiation mechanism for distributed job shop scheduling

This paper presents an agent-based fuzzy constraint-directed negotiation (AFCN) mechanism to solve distributed job shop scheduling problems (JSSPs). The scheduling problem is modelled as a set of fuzzy constraint satisfaction problems (FCSPs), interlinked by inter-agent constraints. Each FCSP represents the perspective of the participants and is governed by autonomous agents. The novelty of the proposed AFCN is to bring the concept of a fuzzy membership function to represent the imprecise preferences of task start time for job and resource agents. This added information sharing is crucial for the effectiveness of distributed coordination. It not only can speed up the convergence, but also enforce a global consistency through iterative exchange of offers and counter-offers. The AFCN mechanism can also flexibly adopt different negotiation strategies, such as competitive, win–win, and collaborative strategies, for different production environments. The experimental results demonstrate that the proposed model can provide not only high-quality and cost-effective job shop scheduling (i.e., comparable to that of centralized methods) but also superior performance in terms of the makespan and average flow time compared with other negotiation models for agent-based manufacturing scheduling. As a result, the proposed AFCN mechanism is flexible and useful for distributed manufacturing scheduling with unforeseen disturbances.

Distributed formation control with relaxed motion requirements

SummaryHeterogeneous formation shape control with interagent bearing and distance constraints involves the design of a distributed control law that ensures the formation moves such that these interagent constraints are achieved and maintained. This paper looks at the design of a distributed control scheme to solve different formation shape control problems in an ambient two‐dimensional space with bearing, distance and mixed bearing and distance constraints. The proposed control law allows the agents in the formation to move in any direction on a half‐plane and guarantees that despite this freedom, the proposed shape control algorithm ensures convergence to a formation shape meeting the prescribed constraints. This work provides an interesting and novel contrast to much of the existing work in formation control where distance‐only constraints are typically maintained and where each agent's motion is typically restricted to follow a very particular path. A stability analysis is sketched, and a number of illustrative examples are also given. Copyright © 2014 John Wiley & Sons, Ltd.

Dynamic Multi-Agent Task Allocation with Spatial and Temporal Constraints

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 due to temporal and spatial constraints that require tasks to be executed sequentially by agents. We propose FMC_TA, a novel task allocation algorithm that allows tasks to be easily sequenced to yield high-quality solutions. FMC_TA first finds allocations that are fair (envy-free), balancing the load and sharing important tasks between agents, and efficient (Pareto optimal) in a simplified version of the problem. It computes such allocations in polynomial or pseudo-polynomial time (centrally or distributedly, respectively) using a Fisher market with agents as buyers and tasks as goods. It then heuristically schedules the allocations, taking into account inter-agent constraints on shared tasks. We empirically compare our algorithm to state-of-the-art incomplete methods, both centralized and distributed, on law enforcement problems inspired by real police logs. The results show a clear advantage for FMC_TA both in total utility and in other measures commonly used by law enforcement authorities.

Bayesian Combinatorial Auctions: Expanding Single Buyer Mechanisms to Many Buyers

We present a general framework for approximately reducing the mechanism design problem for multiple agents to single agent subproblems in the context of Bayesian combinatorial auctions. Our framework can be applied to any setting which roughly satisfies the following assumptions: (i) agents' types are distributed independently (not necessarily identically), (ii) objective function is additively separable over the agents, and (iii) there are no interagent constraints except for the supply constraints (i.e., that the total allocation of each item should not exceed the supply). Our framework is general in the sense that it makes no direct assumption about agents' valuations, type distributions, or single agent constraints (e.g., budget, incentive compatibility, etc.). We present two generic multiagent mechanisms which use single agent mechanisms as black boxes. If an $\alpha$-approximate single agent mechanism is available for each agent, and assuming no agent ever demands more than $\frac{1}{k}$ of all unit...

Decentralized path planning for multi-agent teams with complex constraints

This paper presents a novel approach to address the challenge of planning paths for multi-agent systems subject to complex constraints. The technique, called the Decentralized Multi-Agent Rapidly-exploring Random Tree (DMA-RRT) algorithm, extends the Closed-loop RRT (CL-RRT) algorithm to handle multiple agents while retaining its ability to plan quickly. A core component of the DMA-RRT algorithm is a merit-based token passing coordination strategy that makes use of the tree of feasible trajectories grown in the CL-RRT algorithm to dynamically update the order in which agents replan. The reordering is based on a measure of each agent's incentive to change the plan and allows agents with a greater potential improvement to replan sooner, which is demonstrated to improve the team's overall performance compared to a traditional, scripted replan order. The main contribution of the work is a version of the algorithm, called Cooperative DMA-RRT, which introduces a cooperation strategy that allows an agent to modify its teammates' plans in order to select paths that reduce their combined cost. This modification further improves team performance and avoids certain common deadlock scenarios. The paths generated by both algorithms are proven to satisfy inter-agent constraints, such as collision avoidance, and numerous simulation and experimental results are presented to demonstrate their performance.

Using relaxations to improve search in distributed constraint optimisation

Densely connected distributed constraint optimisation problems (DisCOP) can be difficult to solve optimally, but finding good lower bounds on constraint costs can help to speed up search. We show how good lower bounds can be found by solving relaxed problems obtained by removing inter-agent constraints. We present modifications to the Adopt DisCOP algorithm that allow an arbitrary number of relaxations to be performed prior to solving the original problem. We identify useful relaxations based on the solving structure used by Adopt, and demonstrate that when these relaxations are incorporated as part of the search it can lead to significant performance improvements. In particular, where agents have significant local constraint costs, we achieve over an order of magnitude reduction in messages exchanged between agents. Finally, we identify cases where such relaxation techniques produce less consistent benefits.

Phantom Track Generation Through Cooperative Control of Multiple ECAVs Based on Feasibility Analysis

Radar deception through phantom track generation using multiple electronic combat air vehicles is addressed, which serves as a motivating example for cooperative control of autonomous multiagent systems. A general framework to derive sufficient conditions for the existence of feasible solutions for an affine nonlinear control system comprising of a team of nonholonomic mobile agents having to satisfy actuator and interagent constraints is presented. Based on this feasibility analysis, an algorithm capable of generating trajectories online and in real time, for the phantom track generation problem, is developed. A rigorous treatment of the phantom track generation problem, which includes results on its accessibility, feasibility, local asymptotic straightening of trajectories, and a limited result on system controllability, is given. The basic approach to the algorithm based on the results developed here is presented along with simulation results, validating the proposed approach.

Motion feasibility of multi-agent formations

Formations of multi-agent systems, such as mobile robots, satellites and aircraft, require individual agents to satisfy their kinematic equations while constantly maintaining interagent constraints. In this paper, we develop a systematic framework for studying formation motion feasibility of multi-agent systems. In particular, we consider formations wherein all the agents cooperate to enforce the formation. We determine algebraic conditions that guarantee formation feasibility given the individual agent kinematics. Our framework also enables us to obtain lower dimensional control systems describing the group kinematics while maintaining all formation constraints.

Distributed Asymmetric Constraint Satisfaction, the Phase Transition, and Evolutionary Protocols

Abstract The distributed constraint satisfaction problem (DisCSP) can be viewed as a 4-tuple (X, D, C, A), where X is a set of n variables, D is a set of n domains (one domain for each of the n vaziables), C is a set of constraints that constrain the values that can be assigned to the n variables, and A is a set of agents for which the variables and constraints are distributed. The objective in solving a DisCSP is to allow the agents in A to develop a consistent distributed solution by means of message passing. The constraints are typically considered private and are not allowed to be communicated to fellow agents. When constraint privacy is enforced, inter-agent constraints that exist between the variables of multiple agents will most likely be asymmetric. In this paper, we develop an equation for predicting where the phase transition might be in randomly generated distributed asymmetric CSPs (DisACSPs). We then test this equation by compazing the performances of six evolutionazy protocols referred to as...

CAMPS: a constraint-based architecturefor multiagent planning and scheduling

A new integrated architecture for distributed planning and scheduling is proposed that exploits constraints for problem decomposition and coordination. The goal is to develop an efficient method to solve densely constrained planning/scheduling problems in a distributed manner without sacrificing solution quality. A prototype system (CAMPS) was implemented, in which a set of intelligent agents try to coordinate their actions for ‘satisfying’ planning/scheduling results by handling several intra- and inter-agent constraints. The repair-based methodology for distributed planning/scheduling is described, together with the constraint-based mechanism of dynamic coalition formation among agents.

A multiagent task planning method for agents with disparate capabilities

This paper presents a multiagent task planning method for domains where agents (such as machines and robots) have disparate operational capabilities. The method extends the traditional planning for a solitary agent to handle multiple-agents through a number of novel concepts and planning approaches. The decomposition of the planning problem into subproblems is aimed at achieving plan execution concurrency, and it is based on the assignment of relations (objects may hold in the domain) to agents. A notion of a substate is used to model the plans and actions of the individual agents. To avoid interagent conflicts, interagent constraints provide the coordination between the concurrent actions of the agents and their interactions. Using the substates and the interagent constraints, a decentralised strategy is realised for generating and searching the space of alternative feasible subplans of agents. This strategy is illustrated tith a search space generated for an example problem. The paper also describes the structure of the system used to implement the approach and the concepts.

Rare earth in California

Supply chain planning and scheduling problems require manufacturers (project managers) to determine product configurations, select suppliers (contractors) for task allocation, and schedule project tasks while considering various production constraints among manufacturers and suppliers. Most relevant studies have focused on finding an optimal solution based on complete information provided by each enterprise in a supply chain. However, the practical implementation of complete information sharing is difficult, if not impossible, because of the fully distributed nature of the supply chain process. This work proposes an agent-based fuzzy constraint-directed negotiation (AFCN) model to solve problems associated with supply chain planning and scheduling, which are modeled as a set of fuzzy constraint satisfaction problems (FCSPs) that are interlinked via inter-agent constraints. To accommodate the perspectives and interests of each enterprise in a supply chain, conflicts among FCSPs are resolved using the AFCN protocol through the iterative exchange of offers/counter-offers with limited information sharing and without privacy breaches. A proposed offer/counter-offer represents not only a set of acceptable solutions and preferences for an operational task but also the possibility of conflict in this area. For each FCSP, the incremental process of offer/counter-offer evaluation eliminates redundant and infeasible solutions. The sharing of limited non-strategic sensitive information among agents enables them to elucidate their opponents’ intentions through iterative negotiations, such that the agents can reach an agreement while ensuring that the solution to a project planning and scheduling problem is satisfactory. The AFCN model is also sufficiently flexible to incorporate different negotiation strategies such as competitive, win-win, and collaborative strategies, for various production environments. Herein, a numerical study was conducted to examine the practical viability and effectiveness of the proposed AFCN model. The experimental results show that the proposed AFCN model not only can generate a schedule that is comparable to a near-optimal solution but also is time-efficient. This indicates that the proposed AFCN is a practical and effective method for solving supply chain planning and scheduling problems in fully distributed environments.