Polymorphic self-* agents using game theory for large-scale complex systems

Digitalization and the use of autonomous systems challenge companies in new ways, particularly concerning the automatic distribution of tasks. Various approaches are being discussed in research, specifically in the field of multi-agent systems. However, there does not seem to be a universally applicable and generally accepted solution. Due to the multitude of different approaches and the associated challenges, traceability is of particular interest and requires further analysis. This paper reviews the traceability of different negotiation-based approaches for task allocation in multi-agent systems. We conducted a structured literature review and implemented a prototype using an existing workflow engine for the approach we identified as most suitable, applying a weighted scoring model. The evaluation of the implemented demonstrator, performed both with and without restarting negotiations, indicates that a traceable distribution of tasks with a negotiation mechanism is feasible.

MASQ (Multi-Agent Systems based on Quadrants) is a generic metamodel that integrates many of the concepts issued from the research in the multiagent systems field by defining four perspectives over agent-based interaction according to two axes: internal/external and individual/collective. The aim of this paper is to provide a formal description of MASQ by specifying clear relations between the basic elements of the four quadrants and by analysing the dynamics of a multi-agent system through its transitions and through the classical mind cycle. The proposed formal model has been designed as a theoretical framework and therefore the specifications of many components of a multi-agent system such as the internal architecture of minds, the casual laws of environments or the social laws in organizations and institutions are intentionally left generic to allow for many further concretizations.

The paper considers the approach of using multi-agent models in simulation and decision making of complex systems. Furthermore, an overview of research in the field of multi-agent systems and software platforms for their implementation is carried out. In addition, an analysis of the applicability of intelligent multi-agent systems in the tasks of modelling, optimization and management of energy facilities is performed. This study shows that power industry can use intelligent multi-agent systems not only for the tasks of modelling the interaction of subjects as suppliers and consumers of electricity in the wholesale electricity market and other problems of game theory considering a small number of agents’ types. Moreover, intelligent multi-agent systems can be used to model power systems when it is necessary to take into account the deep integration of the systems. This is understood in the work as dependencies at the level of physical processes of generation, transmission, distribution and consumption of electricity, and at the level of interaction between large technological objects and economic entities. At the second level, it is necessary to consider infrastructure linkages and legal regulations for the economic interaction. As an example, the paper considers a hydropower plant. The entity that manages it must take into account not only the features of the station as a complex electrical facility itself, but also the features of the water usage regime, electricity consumption schedules, meteorological factors, etc. In turn, the use of water resources affects the interests of a large number of subjects, which also needs to be reckoned.

Awareness is a concept that has been frequently studied in the context of Computer Supported Cooperative Work. However, other fields of computer science can benefit from this concept. Recent research in the multi-agent systems field has highlighted the relevance of complex interaction models such as multi-party communication and context awareness for simulation and adaptive systems. In this article, we present a generic interaction model that enables to use these different models in a standardized way. Emerging as a first-order abstraction, the environment, in the sense of a common medium for the agents, is a suitable paradigm to support the agents' awareness. We present an operational model, called Environment as Active Support of Interaction, to take into account all the agents that can be interested in a communication. This model is then extended for the regulation of multiagent systems interactions. Priority policies are given to manage the rules governing the context (un-)awareness of the agents. We also present a new AUML connector to create protocols that take into account the agent awareness to implement proactive behaviour, and several communication scenarios are proposed to show practical applications of this model.

The main challenge that artificial intelligence research is facing nowadays is how to guarantee the development of responsible technology. And, in particular, how to guarantee that autonomy is responsible. The social fears on the actions taken by AI can only be appeased by providing ethical certification and transparency of systems. (See for instance the Barcelona declaration https://www.iiia.csic.es/barcelonadeclaration/) However, this is certainly not an easy task. As we very well know in the multiagent systems field, the prediction accuracy of system outcomes has limits as multiagent systems are actually examples of complex systems. And AI will be social, there will be thousands of AI systems interacting among themselves and with a multitude of humans; AI will necessarily be multiagent. Although we cannot provide complete guarantees on outcomes, we must be able to define with accuracy what autonomous behaviour is acceptable (ethical), to provide repair methods for anomalous behaviour and to explain the rationale of AI decisions. Ideally, we should be able to guarantee responsible behaviour of individual AI systems by construction. I understand by an ethical AI system one that is capable of deciding what are the most convenient norms, abide by them and make them evolve and adapt. The area of multiagent systems has developed a number of theoretical and practical tools that properly combined can provide a path to develop such systems, that is, provide means to build ethical-by-construction systems: agreement technologies to decide on acceptable ethical behaviour, normative frameworks to represent and reason on ethics, and electronic institutions to operationalise ethical interactions. Along my career, I have contributed with tools on these three areas. In this keynote, I will describe a methodology to support their combination that incorporates some new ideas from law, and organisational theory.

In this paper, the paradigm of rationality is examined from the point of view of designing, and possibly implementing, agents endowed with the capacity to interact in a multi-agent environment. The work that will be presented originates from a cross-fertilization between the fields of social simulation and Multi-Agent Systems (MAS) research. In the MAS field, models of intelligent agents in a multi-agent context have been formalized and implemented. The question then arises as to whether the model of rational agents may contribute fruitfully to construct and implement multi-agents systems.

A major challenge in the field of multi-agent systems is to enable autonomous agents to allocate tasks efficiently. In the context of massive multi-agents system (MMAS) which is characterised by a large number of dynamic and heterogeneous agents, traditional tasks allocations approaches based on the negotiation between agents, or a single allocator agent, proved impracticality. In this paper, we propose a decentralised and scalable approach for complex task allocation pro cooperative MMAS. Our approach is based on the idea of grouping agents according to their capacities to execute sub-tasks. Thus, the approach is based on two steps: 1 hierarchical organisation of agent groups using Galois sub-hierarchy, imminent from the formal concepts analysis approach (FCA) 2 computing the optimal allocation. These two steps are fully distributed among agents with the minimum of communication and a grant of finding the optimal allocation in a polynomial time. Further, this paper extends our last approach by distributing the global allocation process among all agents. It provides a solution based on cooperation among agents. This solution prohibits generation of conflicts. It is based on the idea that each agent has to pick out its own sub-task.

A major challenge in the field of Multi-Agent Systems is to enable autonomous agents to allocate tasks efficiently. In previous work, we have developed a decentralized and scalable method for complex tasks allocation for Massive Multi-Agent System (MMAS) based on two steps: 1) hierarchical organization of agent groups using Formal Concepts Analysis approach (FCA), 2) computing the optimal allocation. The first step is computed by one agent named global allocator that computes Galois lattice representing the hierarchical structure of agent groups. Then, it simplifies the completed lattices by pruning unnecessary groups. The second step distributes the tasks allocation process among all agent groups. Nevertheless, the hierarchical organization process is still centralized. Moreover, generation of Galois lattice composed by all concepts (2 min (|O |,|A |) concepts in the worst cases. Where 0 and A means, respectively, the set of objects and the set of attributs) and then simplification of the hierarchy of such size are not useful. This paper extends our last approach to distribute the organization process of agent groups among all agents by providing extension to the Pulton algorithm that generates Galois Sub-Hierarchy which is a polynomial size representation of a concept lattice. This decentralized self-organization of agents provides a flexible infrastructure for agents' dynamicity in MMAS.

Nowadays, in order to face new challenges, extended enterprises must be up to date to the new strategic, economic and organizational structures. Consequently, intelligent software based on agent technology emerges to improve system design, and to increase enterprise competitive position as well. The competitiveness is based on the cooperation. Thus, within this cooperation, conflicts may arise. The automated negotiation plays a key role to look for a common agreement. Argumentation theory has become an important topic in the field of Multi-Agent Systems and especially in the negotiation problem. In this paper, first, an overview of an already proposed model MAIS-E2 (Multi-Agent Information System for an Extended Enterprise) is presented. Then an argumentation based negotiation framework: Relationship-Role and Interest Based Negotiation (R2-IBN) framework is presented, and within this framework, we focused mainly on, argument generation module via inference rules and argument selection module via fuzzy logic.

A survey of modelling and smart management tools for power grids with prolific distributed generation

: Controls research under this program concentrated on the development of a network thermodynamic stabilization framework for hybrid control design of large-scale and multiagent aerospace systems. In particular, we concentrate on hybrid control, hierarchical control, impulsive dynamical systems, nonnegative dynamical systems, compartmental systems, large-scale systems, nonlinear switching control, cooperative control, and adaptive control. Application areas include large flexible interconnected space structures, spacecraft stabilization, cooperative control of unmanned air vehicles, network systems, swarms of air and space vehicle formations, and pharmacological systems.

The article is devoted to the model of the digital technologies introducing in the region's industry. It takes into account the most important factors, such as the behavior of firms, their attitude to digital technologies and the impact of government subsidies. The object of research is the regional industrial complex as a complex adaptive system. The subject of the research is the economic relations emerging in the process of digital transformation of industry. The purpose of the research is to model the processes of digitalization of industry within the framework of the regional industrial complex. The author has used agent-oriented modeling as a methodological tool of the research. The theoretical basis of the research is the theory of complex systems, game theory, theory of diffusion of innovations. The model is implemented in the Anylogic modern software environment. As a result, an agent-oriented model was created; algorithms for digitalization decision-making by industrial enterprises were implemented. We take into account the effects of collective technological transformation. The created version of the model allows by scenario simulations to assess the significance of factors affecting the intensity of digitalization - an increase in the amount of subsidies for digitalization, taking into account the scale of enterprises, industry characteristics, information impact on the heads of industrial enterprises, etc. The model allows us to observe the dynamics of the digital transformation process, to identify peculiar wave-like effects. The specifics of the model are taking into account limited rationality in the behavior of firms, the information interaction between industrial enterprises, time lags in the introduction of digital technologies. The results can be useful to researchers, specialists of industrial enterprises, representatives of government authorities for the formation of a system of incentives for the digital transformation of industry. The obtained results can become the basis for further development of model tools for digitalization management

In this thesis, we introduce and examine four new temporal logic formalisms that can be used as specification languages for the automated verification of the reliability of hardware and software designs with respect to a desired behavior. The work is organized in two parts. In the first one, we reason about two logics for computations, GCTL* and MCTL*, which are useful to describe a correct execution of monolithic closed systems. In the second one, instead, we focus on two logics for strategies, SL and mATL*, which are useful to formalize several interesting properties about interactive plays in multi-entities systems modeled as multi-agent games. In the “Logics for Computations” part, we first study the immersion of the idea of graded quantifications into the temporal-logic framework. In first order logic, existential and universal quantifiers express the concepts of the existence of at least one individual object satisfying a formula, or that all individual objects satisfy a formula. In other logics, these quantifiers have been generalized to express that, for a given non-negative integer n, at least n or all but n individuals satisfy a particular formula. Here, we consider GCTL, a temporal logic with graded path quantifiers, which allows to describe properties like “there exist at least n different classes of computational fluxes in which a system reaches a predetermined state”, where the classes over paths are computed by means of a predetermined equivalence relation. More precisely, we uniformly extend the classic concept of graded quantifiers from states to paths, through the use of a concept of path equivalence with respect to a given path formula. About this logic, in particular, we study the expressiveness and succinctness relationships with respect to GµCalculus and the complexity of the satisfiability problem, which results to be ExpTime-Complete. This research is partially based on the works [BMM09] “Graded Computation Tree Logic” and [BMM10] “Graded Computation Tree Logic with Binary Coding” published, respectively, in the proceedings of the “IEEE Symposium on Logic in Computer Science, 2009” and “EACSL Annual Conference on Computer Science Logic, 2010”. Preliminary results can be also found in [Mog07]. Furthermore, we consider special quantifiers over substructures, which allow to select, using parametric criteria, small critical parts of a system to be successively verified. In literature, there are some attempts to define a logic that allows to modify the underlying structure under exam and then to verify on it some assigned property. However, as far as we know, none of them is able to select minimal submodels of a given property describing the criteria on which then execute the verification process. Here, we base our work on the search of a new operator that merges the concept of quantifiers on structures with that one derived by a generalization of the concept of pruning. The results of this work, is a class of three different extensions of CTL* with minimal model quantifiers, which we name MCTL*. Regarding these logics, we study several reductions among them, as well as the satisfiability problem that we prove to be highly undecidability, i.e., Σ_1^1-Hard, for two out of the three cases. This research is partially based on the work [MM09] “Branching-Time Temporal Logics with Minimal Model Quantifiers” published in the proceedings of the “International Conference on Developments in Language Theory, 2009”. In the “Logics for Strategies” part, we first study the problem of defining a new specification language through which it is possible to express several important properties of multi-entities systems that are neither expressible using classical monolithic temporal logics, such as CTL*, nor using two-agent-teams temporal logics, such as ATL*. In literature, we can found some proposal of logics that try to achieve this goal, but unfortunately, none of them succeeds completely on all the aspects. Among them, one of the most important attempts is CHP-SL, a logics in which one can use variables over strategies. However, this logic has a deep weakness, since it does not allow to describe games with more than two players and even two-players concurrent games. Here, we introduce SL, a logic with a syntax similar in some aspects to the first order logic, in which the strategies of the agent building the game are treated as first order objects on which we can quantify. This logic generalizes CHP-SL, by allowing the specification of the correct behavior of multi-agent concurrent games. In SL, for example, we are able to express very complex but useful Nash equilibria that are not expressible with CHP-SL. We enlighten that Nash equilibrium is one of the most important concepts in game theory. For the introduced logic, we solve two problems left open in the work on CHP-SL. Precisely, we show that the related model-checking problem is 2ExpTime-Complete, thus not harder of the same problem for several subsumed logics, while we prove that its satisfiability problem is highly undecidable, i.e., Σ_1^1-Hard. This research is partially based on the work [MMV10a] “Reasoning About Strategies” published in the proceedings of the “IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science, 2010”. Finally, we consider the concept of relentful strategic reasoning, i.e., a formalism that expresses the ability of a strategy to be used not only to achieve a first given goal, but also to change its final goal in dependence of the history of the play. In the context of planning, memoryful quantification, i.e., quantification over computations that does not lose information about the past along the time, is one of the principal way to express the fact that a system is able to achieve a desired result, and shift to a different goal if some event happens. However, this kind of quantification was not considered before in the context of multi-agent planning. Here, we introduce mATL*, a fusion of the classic alternating temporal logic ATL* with memoryful quantification, with the aim of covering the previous idea. About this logics, we prove that, although it is equivalent to ATL*, it is exponentially more succinct. Nevertheless, we prove that both the model-checking and the satisfiability problems remain 2ExpTime-Complete, as for ATL*. This research is partially based on the work [MMV10b] “Relentful Strategic Reasoning in Alternating-Time Temporal Logic” published in the proceedings of the “International Conference on Logic for Programming Artificial Intelligence and Reasoning, 2010”.

The optimal solution for the cooperation win-win task for Multi-agent system (MAS) was studied and a automatic negotiation reciprocal mechanism was build in this paper for the typical case of multi- agent foraging. The equivalences between agent in MAS and player in game theory, optimal solution in MAS and equilibrium in game theory, and cooperation task in MAS and multi-stage Markov game were then established. To the difficulties of equilibriums selection, a hawk dove game model, which is a uncorrelated asymmetry game, is build from the point view of evolutionary game theory. And a self-enforcing cooperative algorithm based on replicator dynamic was proposed to find the evolutionary stable strategy of hawk dove game. The simulation verified the effectiveness of the proposed approach.

We review applications of p-adic and ultrametric methods in the theory of complex systems. We consider the following examples: the p-adic parameterization of the Parisi matrix in the replica method; the method of hierarchical (interbasin) kinetics, which allows describing macromolecular dynamics by models of ultrametric diffusion; the two-dimensional 2-adic parameterization of the genetic code, which demonstrates that degenerations of the genetic code are described by local constancy domains of maps in the 2-adic metric. We discuss clustering methods for a family of metrics and demonstrate that the multiclustering (ensemble clustering) approach is related to the Bruhat–Tits building theory.