Classical planning problems consist of an environment in a predefined state; a set of deterministic actions that, under certain conditions, change the state of the environment; and a set of goal conditions. A solution to a classical planning problem is a sequence of actions that leads from the initial state to a state satisfying the problem’s goal conditions. There are many methods for finding solutions to classical planning problems, and a popular technique is to exploit structures that commonly occur. One such structure, apparent in many planning domains, is a breakdown of the problem into multiple agents. However, methods for finding and exploiting multiagent structures are not prevalent in the literature and are currently not competitive. This thesis sets out to rectify this problem. Its first main contribution, is to introduce a domain independent algorithm for extracting multiagent structure from classical planning problems. The algorithm relies on identifying a generalisable property of agents in planning; namely, that agents are entities with an internal state, a part of the planning problem that, under a certain distribution of actions, only they can modify. Once this is appropriately formalised, the decomposition algorithm is introduced and is shown to produce identifiably multiagent decompositions over all of the classical planning domains used in the International Planning Competitions, even finding more detailed decompositions than are used by humans in certain cases. Solving multiagent planning problems can be challenging because a solution may require complex inter-agent coordination. The second main contribution of the thesis is a heuristic planning algorithm that effectively exploits the structure of decomposed domains. The algorithm transforms the coordination problem into a process of subgoal generation that can be solved efficiently under a well-known relaxation in planning. The generated subgoals guide the search so that it is always performed by one single-agent subproblem at a time. The algorithm is evaluated and shown to greatly outperform current state-of-the-art planners over decomposable domains. The thesis also includes discussion of the possible extensions of this work, to include the multiagent concepts of self-interested agents and concurrent actions. Results from the multiagent planning literature are improved upon and a new solution concept is presented that accounts for the ‘farsightedness’ inherent in planning. A method is then presented that can find stable solutions for a certain class of multiagent planning problems. A new method is introduced for modelling concurrent actions that allows them to be written without requiring knowledge of each other agent in the domain, and it is shown how such problems can be solved by a translation to single-agent planning.

在过去的十年间，多智能体系统的协作控制问题引起了广泛的关注。为了解决趋同、编队、蜂拥、群聚等多智能体的协作控制问题，许多研究者提出了各种各样的集中式和分布式控制器。但是这些结果大多是针对线性的多智能体系统的，本论文将利用一些非线性技术去研究线性和非线性的多智能体系统的协作控制问题。 / 1. 有领导者的保持连接的群聚问题: 这类问题的研究主要是针对单点积分器和二重积分器的多智能体系统。为了保持网络的原始链接，我们引入了有界的势能函数，基于这样的势能函数，我们提出了非线性的控制器，所以尽管这样的多智能体系统本身是线性的，但闭环系统是非性的。因此我们利用李雅普诺夫定理来分析闭环系统的性能,并进行了大量的仿真实验来衡量我们的控制器的有效性。具体的结果列如下: / 我们首先研究的系统是带领导的单点积分器的多智能体系统，其中领导是由线性自治系统生成。现有的结果只能处理领导者信号是恒定的或者是斜波信号。而我们提出了一个分布式的状态反馈的控制器，不管领导者的信号是阶跃，斜波还是正弦信号，我们提出的这一控制器都能保持整个系统的原始连接，并且同时能实现各个子系统对领导者的渐近跟踪。 / 我们并进一步研究了二重积分器的多智能体系统，而且这样的系统受到外部信号的干扰。领导者的信号和干扰信号可以是阶跃信号，斜波信号以及具有任意振幅和初始相位的正弦信号的组合。受到一些输出调节理论的启发，我们同时提出了分布式的全状态反馈控制器和带有分布式观测器的输出反馈控制器。尽管存在外部干扰信号，这两种控制器都能保持整个系统的初始连接，同时能实现各个子系统对领导者的渐近跟踪的目标。 / 值得注意的是尽管我们研究是多智能体系统的群聚问题，这种技术同时能用来解决其他类似的编队、蜂拥等协作控制问题。 / 2. 非线性多智能体系统的合作输出调节问题: 我们首先明确地提出了什么是非线性多智能体系统的合作输出调节问题。这个问题可以看作是有领导者的趋同问题的一般化。这个非线性多智能体系统包含了一个领导者和各个子系统，其中领导者的信号由一外部线性自治系统产生，而每个子系统是含有不确定参数的非线性系统，并且这些子系统受到外部信号的干扰。首先我们引入分布式的内模，然后通过坐标变换，得到了一个多输入多输出的增广系统，之后我们把非线性多智能体系统的合作输出调节问题转化成了这个增广系统的全局镇定问题，最后一系列标准的假设下，我们提出了一分布式输出反馈控制器解决了镇定问题，从而解决了输出调节问题。具体来说，假设通信图是连接的，如果我们能解决每个子系统的输出调节问题，那我们提出的分布式输出反馈调节器就能解决这个多智能体系统的合作输出调节问题。我们也把提出的这一控制器应用于洛伦兹多智能体系统的合作输出调节问题。 / Over the past decade, the coordinated control problems for multi-agent systems have attracted extensive attention. Both centralized and distributed control protocols have been developed to study such multi-agent coordinated control problems as consensus, formation, swarming, flocking, rendezvous and so on. However, most papers employ standard linear control techniques. The results are mainly limited to linear multi-agent systems. In this thesis, we will study some coordinated control problems of both linear and nonlinear multi-agent systems by some advanced nonlinear techniques. / This thesis has mainly studied two problems. / i) The leader-following rendezvous with connectivity preservation. We have studied this problem for both single integrator and double integrator multi-agent systems by nonlinear control laws utilizing bounded potential function. Although the model of multi-agent system is linear, the closed-loop system is nonlinear due to the employment of nonlinear control laws. We have developed a Lyapunov-based method to analyze the performance of the closed-loop system, and conducted extensive simulations to evaluate the effectiveness of our control schemes. The specific results are summarized as follows. / We have studied the case where the leader system is a linear autonomous system and the follower system is a multiple single-integrator system. The existing results can only handle the case where the leader signal is a constant signal or ramp signal and the control law is discontinuous. By introducing an exosystem, we have proposed a distributed state feedback smooth control law. For a class of reference signals such as step, ramp, and sinusoidal signals, our control law is able to maintain the connectivity of the system and, at the same time, achieve asymptotic tracking of all followers to the output of the leader system. / We have also studied a leader-following rendezvous problem for a double integrator multi-agent system subject to external disturbances. Both the leader signal and disturbance signal can be a combination of step signal, ramp signal and sinusoidal signal with arbitrary amplitudes and initial phases. Motivated by some techniques in output regulation theory, we have developed both distributed state feedback control protocol and distributed output feedback control protocol which utilizes a distributed observer. Both of our control laws are able to maintain the connectivity of an initially connected communication network, and, at the same time, achieve the objective of the asymptotic tracking of all followers to the leader regardless of external disturbances. / It is noted that even though we have only studied the rendezvous problem, the techniques of this thesis can also be used to handle other similar problems such as formation, flocking, swarming, etc. / ii) Cooperative output regulation problem of nonlinear multi-agent systems. We have formulated the cooperative output regulation problem for nonlinear multi-agent systems. The problem can be viewed as a generalization of the leader-following consensus/ synchronization problem in that the leader signals are a class of signals generated by an exosystem, each follower subsystem can be subject to a class of external disturbances, and individual follower subsystems and the leader system have different dynamics. We first show that the problem can be converted into the global stabilization problem of a class of multi-input, multi-output nonlinear systems called augmented system via a set of distributed internal models. Then we further show that, under a set of standard assumptions, the augmented system can be globally stabilized by a distributed output feedback control law. We have solved the cooperative output regulation problem of uncertain nonlinear multi-agent systems in output feedback form. The main result can be summarized as follows: assuming the communication graph is connected, then the problem can be solved by a distributed output feedback control law if the global robust output regulation problem for each subsystem can be solved by an output feedback control law. We have also applied our approach to solve a leader-following synchronization problem for a group of Lorenz multi-agent systems. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Dong, Yi. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 102-111). / Abstract also in Chinese. / Abstract --- p.i / Acknowledgement --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Literature Review --- p.1 / Chapter 1.1.1 --- Leader-following rendezvous with connectivity preservation problem --- p.3 / Chapter 1.1.2 --- Cooperative output regulation problem of nonlinear multi-agent systems --- p.4 / Chapter 1.2 --- Thesis Contributions --- p.4 / Chapter 1.3 --- Thesis Organization --- p.6 / Chapter 2 --- Fundamentals --- p.8 / Chapter 2.1 --- Review of Graph Theory Notation --- p.8 / Chapter 2.2 --- Review of Linear Output Regulation --- p.9 / Chapter 2.2.1 --- Regulator equations --- p.10 / Chapter 2.2.2 --- Linear feedback control laws --- p.11 / Chapter 2.2.3 --- Barbalat’s Lemma --- p.12 / Chapter 2.3 --- Review of Nonlinear Output Regulation --- p.12 / Chapter 2.3.1 --- From nonlinear output regulation to stabilization --- p.13 / Chapter 2.3.2 --- Construction of internal model --- p.15 / Chapter 2.3.3 --- Some theories --- p.17 / Chapter 3 --- Leader-following Rendezvous with Connectivity Preservation of Single-integrator Multi-agent Systems --- p.19 / Chapter 3.1 --- Introduction --- p.19 / Chapter 3.2 --- Problem Formulation --- p.20 / Chapter 3.3 --- Solvability of Problem --- p.22 / Chapter 3.4 --- Example --- p.28 / Chapter 3.5 --- Conclusion --- p.28 / Chapter 4 --- A Leader-following Rendezvous Problem of Double Integrator Multiagent Systems --- p.30 / Chapter 4.1 --- Introduction --- p.30 / Chapter 4.2 --- Problem Formulation --- p.32 / Chapter 4.3 --- Main Result --- p.34 / Chapter 4.4 --- Illustrative Examples --- p.41 / Chapter 4.4.1 --- Example 1 --- p.41 / Chapter 4.4.2 --- Example 2 --- p.42 / Chapter 4.5 --- Conclusion --- p.43 / Chapter 5 --- Leader-following Connectivity Preservation Rendezvous of Multi-agent Systems Based Only Position Measurements --- p.46 / Chapter 5.1 --- Introduction --- p.46 / Chapter 5.2 --- Problem Formulation --- p.47 / Chapter 5.3 --- Construction of Distributed Controller --- p.49 / Chapter 5.4 --- Example --- p.55 / Chapter 5.5 --- Conclusion --- p.58 / Chapter 6 --- Cooperative Global Robust Output Regulation for Nonlinear Multiagent Systems in Output Feedback Form --- p.61 / Chapter 6.1 --- Introduction --- p.61 / Chapter 6.2 --- Preliminaries --- p.63 / Chapter 6.3 --- Construction of Distributed Controller --- p.66 / Chapter 6.4 --- Application to Lorenz Multi-agent Systems --- p.69 / Chapter 6.5 --- Conclusion --- p.72 / Chapter 7 --- Cooperative Global Output Regulation for a Class of Nonlinear Multiagent Systems --- p.75 / Chapter 7.1 --- Introduction --- p.75 / Chapter 7.2 --- Preliminaries --- p.77 / Chapter 7.3 --- Solvability of Problem --- p.82 / Chapter 7.4 --- Application to Hyper-Chaotic Lorenz Multi-agent Systems --- p.90 / Chapter 7.5 --- Concluding Remarks --- p.97 / Chapter 8 --- Conclusions and Future Work --- p.100 / Chapter 8.1 --- Conclusions --- p.100 / Chapter 8.2 --- Future Work --- p.101 / Bibliography --- p.102 / Biography --- p.112

In this thesis we study the problem of how to effectively manage and operate a market that attracts trading agents to compete for resources in it. In order to attract more agents to the market, the market needs to have incentive policies. We are particularly interested in the research of the incentive matching policy. We propose a new matching policy with loyalty incentive features. In order to cooperate and improve its performance, we also propose a new accepting policy to work with the matching policy. We use the CAT platform as our test-bed. We describe all the policies and techniques used in the CAT competition in detail. In addition we carry out experiments which further support our proposal.

Managing large scale supply chains are never an easy task. Numerous researches have put emphasis on supply chain modeling and optimization to assist businesses in searching for the best practices so as to endure the extremely competitive business landscape. To some, the paradigm of centralized supply chain management is adequate for solving its strategic and operational problems. Yet with the improper use of authoritative assumptions, the efficiency of the management process is often jeopardized. Furthermore, current researches in decentralized supply chain are mostly focused on dyadic or linear relationship and seldom consider quantitative modeling and analysis with scalability. Recent development in multi-agent systems provided a means for such a modeling methodology and hence researches in this area. To enhance model representativeness and computational efficiency, vision-based control models that are able to simulate individual operational and strategic traits are developed. In this research, pyramidal agent alignment is proposed for simulating the management-operation dimension with regards to decision exercising and bargaining power management. The system offers one thousand supply chain agents that are simulated in a mono-layer, multi-tier network in real time. Stochastic and dynamic behaviors of the network are handled by statistical regression on scenario-based model evaluation. The proposed design enabled grand scale supply chain modeling and optimization that follows a general or custom simulation supported optimization architecture. Network governance problems and dynamic steering problems are considered and solved using genetic algorithm and dynamic programming. The thesis looks into the potential benefits and limitations of the proposed methods in details, and future research directions are discussed. / published_or_final_version / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy

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In this thesis we study the problem of how to effectively manage and operate a market that attracts trading agents to compete for resources in it. In order to attract more agents to the market, the market needs to have incentive policies. We are particularly interested in the research of the incentive matching policy. We propose a new matching policy with loyalty incentive features. In order to cooperate and improve its performance, we also propose a new accepting policy to work with the matching policy. We use the CAT platform as our test-bed. We describe all the policies and techniques used in the CAT competition in detail. In addition we carry out experiments which further support our proposal.

Ph.D. (Computer Science) / The development of software systems is a relatively recent field of human endeavour. Even so, it has followed a steady progression of dominant paradigms which have incrementally improved the ease with which developers are able to express the logic and structure of their systems. The initially unstructured era of free-form spaghetti code gave way to structured programming in which the entry and exit points of functional units were well defined through the creation of abstractions such as procedures, sub-routines and functions. The problem of correctly associating data with the set of operations which are legal on this data was addressed through the concept of encapsulation with the onset of object-oriented programming. Object orientation also introduced a set of abstractions for safe code reuse through inheritance and dynamic polymorphism as well as composition/aggregation and delegation. The agent-oriented software development paradigm, when viewed as an extension of object orientation, adds the capacity of agent autonomy to an object, which allows it to select for itself which of its operations it will execute at any point in time. In addition, the separation between an agent and the environment within which it is embedded must be well defined. Agent autonomy allows for the modelling and development of loosely coupled systems with the capacity for complex emergent behaviour. The mapping of a given set of environmental percepts to an agent's operation selection defines its agent function and hence its emergent behaviour. Furthermore, agents may also be embedded into a shared environment together with other agents forming a multi-agent system. The emergent characteristics of such systems are defined not only through changes in environment state but also via agent to agent interactions. Multi-agent systems are categorised into cooperative or competitive based on whether all the agents within the system share a common goal. An argument is presented that even within cooperative multi-agent systems selfishness will emerge as a direct consequence of computational intractability. The core of the argument centres on the finite nature of the computational resources available to an agent which must be divided between the evaluation of the usefulness of other agent's knowledge and intentions towards improving the collective utility of the system and directly acting upon its own. As a direct result of the halting problem it is impossible for an agent to ascertain in general whether another agent's plans are even feasible (i.e. will result in the system reaching a goal state). As a direct consequence of such a limitation agents will in general favour their own courses of action over those of others and hence an emergent selfishness occurs even in ostensibly cooperative systems...

[EN] Over the last years, multiagent systems have been proven to be a powerful and versatile paradigm, with a big potential when it comes to solving complex problems in dynamic and distributed environments, due to their flexible and adaptive behavior. This potential does not only come from the individual features of agents (such as autonomy, reactivity or reasoning power), but also to their capability to communicate, cooperate and coordinate in order to fulfill their goals. In fact, it is this social behavior what makes multiagent systems so powerful, much more than the individual capabilities of agents. The social behavior of multiagent systems is usually developed by means of high level abstractions, protocols and languages, which normally rely on (or at least, benefit from) agents being able to communicate and interact indirectly. However, in the development process, such high level concepts habitually become weakly supported, with mechanisms such as traditional messaging, massive broadcasting, blackboard systems or ad hoc solutions. This lack of an appropriate way to support indirect communication in actual multiagent systems compromises their potential. This PhD thesis proposes the use of event tracing as a flexible, effective and efficient support for indirect interaction and communication in multiagent systems. The main contribution of this thesis is TRAMMAS, a generic, abstract model for event tracing support in multiagent systems. The model allows all entities in the system to share their information as trace events, so that any other entity which require this information is able to receive it. Along with the model, the thesis also presents an abstract architecture, which redefines the model in terms of a set of tracing facilities that can be then easily incorporated to an actual multiagent platform. This architecture follows a service-oriented approach, so that the tracing facilities are provided in the same way than other traditional services offered by the platform. In this way, event tracing can be considered as an additional information provider for entities in the multiagent system, and as such, it can be integrated from the earliest stages of the development process. / [ES] A lo largo de los últimos años, los sistemas multiagente han demostrado ser un paradigma potente y versátil, con un gran potencial a la hora de resolver problemas complejos en entornos dinámicos y distribuidos, gracias a su comportamiento flexible y adaptativo. Este potencial no es debido únicamente a las características individuales de los agentes (como son su autonomía, y su capacidades de reacción y de razonamiento), sino que también se debe a su capacidad de comunicación y cooperación a la hora de conseguir sus objetivos. De hecho, por encima de la capacidad individual de los agentes, es este comportamiento social el que dota de potencial a los sistemas multiagente. El comportamiento social de los sistemas multiagente suele desarrollarse empleando abstracciones, protocolos y lenguajes de alto nivel, los cuales, a su vez, se basan normalmente en la capacidad para comunicarse e interactuar de manera indirecta de los agentes (o como mínimo, se benefician en gran medida de dicha capacidad). Sin embargo, en el proceso de desarrollo software, estos conceptos de alto nivel son soportados habitualmente de manera débil, mediante mecanismos como la mensajería tradicional, la difusión masiva, o el uso de pizarras, o mediante soluciones totalmente ad hoc. Esta carencia de un soporte genérico y apropiado para la comunicación indirecta en los sistemas multiagente reales compromete su potencial. Esta tesis doctoral propone el uso del trazado de eventos como un soporte flexible, efectivo y eficiente para la comunicación indirecta en sistemas multiagente. La principal contribución de esta tesis es TRAMMAS, un modelo genérico y abstracto para dar soporte al trazado de eventos en sistemas multiagente. El modelo permite a cualquier entidad del sistema compartir su información en forma de eventos de traza, de tal manera que cualquier otra entidad que requiera esta información sea capaz de recibirla. Junto con el modelo, la tesis también presenta una arquitectura {abs}{trac}{ta}, que redefine el modelo como un conjunto de funcionalidades que pueden ser fácilmente incorporadas a una plataforma multiagente real. Esta arquitectura sigue un enfoque orientado a servicios, de modo que las funcionalidades de traza son ofrecidas por parte de la plataforma de manera similar a los servicios tradicionales. De esta forma, el trazado de eventos puede ser considerado como una fuente adicional de información para las entidades del sistema multiagente y, como tal, puede integrarse en el proceso de desarrollo software desde sus primeras etapas. / [CAT] Al llarg dels últims anys, els sistemes multiagent han demostrat ser un paradigma potent i versàtil, amb un gran potencial a l'hora de resoldre problemes complexes a entorns dinàmics i distribuïts, gràcies al seu comportament flexible i adaptatiu. Aquest potencial no és només degut a les característiques individuals dels agents (com són la seua autonomia, i les capacitats de reacció i raonament), sinó també a la seua capacitat de comunicació i cooperació a l'hora d'aconseguir els seus objectius. De fet, per damunt de la capacitat individual dels agents, es aquest comportament social el que dóna potencial als sistemes multiagent. El comportament social dels sistemes multiagent solen desenvolupar-se utilitzant abstraccions, protocols i llenguatges d'alt nivell, els quals, al seu torn, es basen normalment a la capacitat dels agents de comunicar-se i interactuar de manera indirecta (o com a mínim, es beneficien en gran mesura d'aquesta capacitat). Tanmateix, al procés de desenvolupament software, aquests conceptes d'alt nivell son suportats habitualment d'una manera dèbil, mitjançant mecanismes com la missatgeria tradicional, la difusió massiva o l'ús de pissarres, o mitjançant solucions totalment ad hoc. Aquesta carència d'un suport genèric i apropiat per a la comunicació indirecta als sistemes multiagent reals compromet el seu potencial. Aquesta tesi doctoral proposa l'ús del traçat d'esdeveniments com un suport flexible, efectiu i eficient per a la comunicació indirecta a sistemes multiagent. La principal contribució d'aquesta tesi és TRAMMAS, un model genèric i abstracte per a donar suport al traçat d'esdeveniments a sistemes multiagent. El model permet a qualsevol entitat del sistema compartir la seua informació amb la forma d'esdeveniments de traça, de tal forma que qualsevol altra entitat que necessite aquesta informació siga capaç de rebre-la. Junt amb el model, la tesi també presenta una arquitectura abstracta, que redefineix el model com un conjunt de funcionalitats que poden ser fàcilment incorporades a una plataforma multiagent real. Aquesta arquitectura segueix un enfoc orientat a serveis, de manera que les funcionalitats de traça són oferides per part de la plataforma de manera similar als serveis tradicionals. D'aquesta manera, el traçat d'esdeveniments pot ser considerat com una font addicional d'informació per a les entitats del sistema multiagent, i com a tal, pot integrar-se al procés de desenvolupament software des de les seues primeres etapes. / Búrdalo Rapa, LA. (2016). TRAMMAS: Enhancing Communication in Multiagent Systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/61765 / TESIS

Doctor of Philosophy / Department of Computing and Information Sciences / Scott DeLoach / A multiagent system is a system that is composed of multiple autonomous agents. Au- tonomous agents are given the right and the responsibility to make decisions based on their perceptions and goals. Agents are also constrained by their capabilities, the environment and the system with which they reside. An agent within the system may need to coordinate with another agent in the system. This coordination may allow the agent to give updates from sensor readings, communicate updated map information or allow the agent to work on a cooperative task such as lifting an object. To coordinate agents must be able to communicate with one another. To communicate agents must have a communication medium. The medium is the conduit through which the information flows. Additionally there must be a set of rules to govern which agent talks at what time. This set of rules is called a communication protocol. To effectively and efficiently communicate all agents participating in the communication must be using compatible protocols. Robotic agents can be placed in diverse environment and there are multiple avenues for communication failure. Current multiagent systems use fixed communication protocols to allow agents to interact with one another. Using fixed protocols in an error prone environ- ment can lead to a high rate of system failure. To address these issues, I propose that a formal framework for interaction be defined. The framework should allow agents to choose new interaction protocols when the current protocol they are using fails. A formal framework allows automated tools to reason over the possible choices of interaction protocols. The tools can enumerate the protocols that will allow the agent to achieve its desired goal.

Through cooperation, agents can transcend their individual capabilities and achieve goals that would be unattainable otherwise. Existing multiagent planning work considers each agent’s action capabilities, but does not account for distributed knowledge and the incompatible views agents may have of the planning domain. These divergent views can be a result of faulty sensors, local and incomplete knowledge, and outdated information, or simply because each agent has conducted different inferences and their beliefs are not aligned. This thesis is concerned with Multi-Perspective Cooperative Planning (MPCP), the problem of synthesising a plan for multiple agents which share a goal but hold different views about the state of the environment and the specification of the actions they can perform to affect it. Reaching agreement on a mutually acceptable plan is important, since cautious autonomous agents will not subscribe to plans that they individually believe to be inappropriate or even potentially hazardous. We specify the MPCP problem by adapting standard set-theoretic planning notation. Based on argumentation theory we define a new notion of plan acceptability, and introduce a novel formalism that combines defeasible logic programming and situation calculus that enables the succinct axiomatisation of contradictory planning theories and allows deductive argumentation-based inference. Our work bridges research in argumentation, reasoning about action and classical planning. We present practical methods for reasoning and planning with MPCP problems that exploit the inherent structure of planning domains and efficient planning heuristics. Finally, in order to allow distribution of tasks, we introduce a family of argumentation-based dialogue protocols that enable the agents to reach agreement on plans in a decentralised manner. Based on the concrete foundation of deductive argumentation we analytically investigate important properties of our methods illustrating the correctness of the proposed planning mechanisms. We also empirically evaluate the efficiency of our algorithms in benchmark planning domains. Our results illustrate that our methods can synthesise acceptable plans within reasonable time in large-scale domains, while maintaining a level of expressiveness comparable to that of modern automated planning.