Knowledge in multiagent systems

This paper presents a multi agent system which provides a method for cooperation and coordination among agents using collective reasoning. There are two types of agents in the system: distributed agents and a central administrator agent. The distributed agents provide local knowledge extraction tasks and communicate it to the central administrator agent. The central administrator agent builds a global picture about the problem domain using the local knowledge of each agent. It uses the Growing Self Organizing Map (GSOM), which is a dynamic version of the Self Organizing Map (SOM) for analyzing the global knowledge. The system differs from similar systems which employ central administrator functionality as the central administrator agent in the proposed system is capable of autonomously refining the task model of the distributed agents and dynamically determining the efficient number of agents required in the system.

Agents in a multiagent system (MAS) could cooperate in many different ways. The topology of agent cooperation determines how the agents control and communicate with each other, what are the control and communication capabilities of each agent, and how efficient the control and communications are. The topology affects the agents' ability to share knowledge, integrate knowledge, and make efficient use of knowledge in MAS. We first present an overview of three major MAS topologic models, assesses their advantages and disadvantages in terms of agent autonomy, adaptation, scalability, and efficiency of cooperation. A fourth model of topology is then introduced that combines some of the features of previous models. A design example applying the new model for information service management is presented.

When giving an analysis of knowledge in multiagent systems, one needs a framework in which higher-order information and its dynamics can both be represented. A recent tradition starting in original work by Plaza treats all of knowledge, higher-order knowledge, and its dynamics on the same foot. Our work is in that tradition. It also fits in approaches that not only dynamize the epistemics, but also epistemize the dynamics: the actions that (groups of) agents perform are epistemic actions. Different agents may have different information about which action is taking place, including higher-order information. We demonstrate that such information changes require subtle descriptions. Our contribution is to provide a complete axiomatization for an action language of van Ditmarsch, where an action is interpreted as a relation between epistemic states (pointed models) and sets of epistemic states. The applicability of the framework is found in every context where multiagent strategic decision making is at stake, and already demonstrated in game-like scenarios such as Cluedo and card games.

What do epistemic logic and cognitive science have to do with each other?

In this paper we present two developed multi-agent architectures. The first one models knowledge-based support of the eServices in the domain of home care, while the second one represents a general model that uses procedural knowledge in the form of organizational processes and formalized medical guidelines in a decision support system. We show that integration of both architectures is feasible and moreover, it can enhance functionality of the home care system.

This chapter contains sections titled: Runs and Systems, Incorporating Knowledge, Incorporating Time, Examples of Systems, Knowledge Gain in A.M.P. Systems, Exercises, Notes

The development in the recent decade has proven that the multi-agent paradigm represents a challenging framework for solving very complex tasks of cooperation in virtual organizations. Each partner/unit engaged in a virtual organization can be considered as an autonomous unit with its own resources, knowledge and goals and represented by a corresponding software module—a software agent.

Propagation of Action Knowledge in Multi-Agent Systems

This contribution presents a knowledge-based model of the agents' mutual awareness (social knowledge) and justifies its role in various classes of applications of the concept of multi-agent systems. A tri-base acquaintance model (3bA) has been formalized and designed in order to limit explosive com munication space in multi-agent systems. As a formal model of agent's social behaviour knowledge and agent's cooperation neighbourhood knowledge, the 3bA model approach provides MAS designers with knowledge structures and knowledge maintenance mechanism. Applications of the 3bA model to the following areas are commented: production planning, supply chain management, coalition formation and intrusion detection. The study concludes with a unifying view on social knowledge structuring.

Group Announcement Logic (GAL) and Coalition Announcement Logic (CAL) were proposed to study effects of public announcements by groups of agents on knowledge in multiagent systems. Both logics have operators that quantify over such announcements. In GAL, it is possible to express that 'a group of agents G has a (truthful) announcement such that after this announcement, some property A holds'; for example, A may involve some agents in G gaining additional knowledge, while agents outside G remain ignorant. In CAL, the meaning of the coalition announcement operator is subtly different: it says that 'G has an announcement such that, whatever else the agents outside G announce simultaneously, some property A is guaranteed to hold after the joint announcement'. It has been open for some time whether GAL and CAL are equally expressive. We show that this is not the case: there is a property expressible in GAL that is not expressible in CAL. It is still an open question whether CAL is subsumed by GAL, or whether the two logics have incomparable expressive power.

The research focuses on methods and software tools for decision support for the organization of distributed production of unmanned aerial vehicles (UAVs) based on digital twin and multi-agent systems technologies. The purpose of the work is to provide a critical review of existing approaches to the organization of distributed production of UAVs, as complex technical objects, and to substantiate an approach to building a multi-agent decision support system for the synthesis of a rational production structure based on digital twins of participants in production cooperation. The tasks include: classifying unmanned aerial vehicles according to the main features that determine their production specifics; analysing modern scientific publications in the field of distributed production informatization, multi-agent systems, digital twins and intelligent decision support; identifying the limitations of existing solutions; and proposing an approach to the organization of distributed production of UAVs using digital twins in the form of intelligent agents. The applied methods are based on system and classification analysis, generalization of modern scientific approaches to the organization of production, decision-making theory, the concept of multi-agent systems, digital twin technologies, production logistics and artificial intelligence. The following results were obtained: UAVs were classified by mass, purpose and level of autonomy, which made it possible to determine the specifics of their production and life cycle management. An analysis of scientific publications was performed, which showed the active development of areas related to the use of multi-agent systems, digital twins and artificial intelligence to address complex tasks in UAV life cycle management. It was established, in particular, that the works of recent years consider the issues of cooperative UAV production, the use of deep learning algorithms for decision-making in dynamic environments and the integration of digital twins into relevant production systems. At the same time, a number of limitations on the implementation of existing solutions were identified: insufficient formalization of business processes between production participants, weak integration of digital twins into production logistics, and the lack of self-organization mechanisms. An approach to solving the problem of effective organization of distributed UAV production is proposed. The architecture of a multi-agent decision support system for distributed UAV production is developed, which includes modules for coordination, self-organization, and the evaluation of the effectiveness of production configuration options, considering the initial risk tolerance indicator. Conclusions. The scientific novelty of the results obtained is as follows: an approach to solving the problem of effective organization of distributed UAV production is proposed, which, unlike existing ones, is based on the representation of production participants as digital twins in the form of intelligent agents, this enables the generation of multiple options and the subsequent selection of the most rational configuration for organizing distributed UAV production in terms of business process efficiency and the reduction of production risks. The research results create a basis for further integration of multi-agent technologies into real-world manufacturing platforms and the expansion of their application to other types of complex machinery.

A key difficulty in the design of multi-agent robotic systems is the size and complexity of the space of possible designs. In order to make principled design decisions, an understanding of the many possible system configurations is essential. To this end, we present a taxonomy that classifies multi-agent systems according to communication, computational and other capabilities. We survey existing efforts involving multi-agent systems according to their positions in the taxonomy. We also present additional results concerning multi-agent systems, with the dual purposes of illustrating the usefulness of the taxonomy in simplifying discourse about robot collective properties, and also demonstrating that a collective can be demonstrably more powerful than a single unit of the collective.

The traditional method of designing the initial configuration of off-axis reflective optical system is to first obtain the initial configuration of coaxial reflective optical system, and then achieve the unobscured design with an offset aperture stop or a biased input field, or both. Because the aberration distribution of coaxial reflective optical system is not applicable to the off-axis reflective optical system, the obtained unobscured off-axis reflective optical system has large aberration, and the unobscured design process is complicated. In this paper we present a method of designing an initial configuration of off-axis reflective optical system based on vector aberration theory. With this design method, a good unobscured initial configuration of off-axis reflective optical system can be directly obtained by using an offset aperture stop or a biased input field, or both. Based on the vector aberration theory and gaussian brackets, the third-order aberration coefficient is derived for off-axis reflective optical system. Initial configuration performance is important for optical design, especially for the complicated optical system design. The selection of initial configuration highly affects the final system imaging performance, fabrication difficulty and alignment difficulty. An error function is established to evaluate the performance of off-axis reflective optical system, and it consists of aberration coefficients and other constraints. The genetic algorithm is a highly parallel, random and adaptive global optimization algorithm. To obtain a good initial configuration for the off-axis reflective optical system, the genetic algorithm is used to search for the initial configuration with minimum residual aberration. This method can obtain a good initial configuration of off-axis reflective optical system for further optimization. The benefit of this design method is demonstrated by designing an off-axis three-mirror optical system. For the focal plane array, a long-wave infrared off-axis three-mirror optical system is designed. A good initial configuration is obtained with the proposed method, which achieves the unobscured design by using an offset aperture stop and a biased input field. To improve the performance of initial configuration, the obtained initial configuration is optimized with the optical design software. The designed optical system has good imaging quality. As the mirrors are free from the tilts and decenters, the designed optical system is aligned easily.

In logics for strategic reasoning the main challenge is represented by their verification in contexts of imperfect information and perfect recall strategies. In this work, we show a technique to approximate the verification of Alternating-time Temporal Logic (ATL*) under imperfect information and perfect recall, which is known to be undecidable. Given a model M and a formula phi, we propose a verification procedure that generates sub-models of M in which each sub-model M' satisfies a sub-formula phi' of phi and the verification of phi' in M' is decidable. Then, we use CTL* model checking to provide a verification result of phi on M. We prove that our procedure is sound and in the same complexity class of ATL* model checking under perfect information and perfect recall. Moreover, we present a tool that uses our procedure and provide experimental results.

We investigate the verification of Multi-agent Systems against strategic properties expressed in Alternating-time Temporal Logic under the assumptions of imperfect information and perfect recall. To this end, we develop a three-valued semantics for concurrent game structures upon which we define an abstraction method. We prove that concurrent game structures with imperfect information admit perfect information abstractions that preserve three-valued satisfaction. Further, we present a refinement procedure to deal with cases where the value of a specification is undefined. We illustrate the overall procedure in a variant of the Train Gate Controller scenario under imperfect information and perfect recall.