Multi-agent coordination through coalition formation

This work focuses on coalition formation among heterogeneous agents for the 2017 multiagent programming contest. An agent is a computer system that is capable of independent action to achieve its goals. In order to increase the effectiveness of the agents, we can organise them into coalitions, in which the agents collaborate with each other to achieve individual or common goals. We investigate and apply coalition structure generation (the first activity of the coalition formation process) in simulated scenarios, specifically the 2017 contest scenario, where the agents forming a competing team cooperate to solve logistic problems simulated on the map of a real city. In order to achieve our goal, we integrate coalition formation algorithms into the JaCaMo platform by means of a CArtAgO artefact, named CFArtefact. We use the implementation of the SMART JaCaMo team for experimenting with the coalition formation approach in the contest scenario. We experiment on three approaches in the contest domain with different configurations. In the first, we use only a taskallocation mechanism, while the other approaches use an optimal coalition formation algorithm and a heuristic coalition formation algorithm. We conducted several experiments to compare the advantages of each approach. Our results show that coalition formation algorithms can improve the performance of a participating team when dealing with low job rates (i.e., how quickly new jobs are created by the simulation). However, as we increase the job rate, the approach using only task allocation has better performance. Even a heuristic coalition formation approach has close performance to the optimal one in that case. Coalition formation can play an important role when we aim to balance each group of agents to accomplish some particular goal given a larger team of cooperating agents.

Selective Exploration Algorithm for Coalition Formation

We consider a cognitive radio setting in which a set of secondary transmitter-receiver pairs coexist with a primary user. In the channel sensing phase, each secondary system senses the primary user's channel to determine its occupancy. The existence of a fusion center enables joint processing of the sensing data. This would improve the joint sensing performance, however at the cost of a delay required for reporting the sensed data. In the channel access phase, noncooperative secondary transmission can lead to a degradation of secondary users' average achievable rates due to excessive mutual interference. We propose to coordinate the secondary system transmission by providing orthogonal multiple access rules for the secondary users which report their sensing data to the fusion center. Using models from game theory, a coalition formation algorithm is proposed to find a stable set of secondary users participating in sensing data fusion. It is shown by simulation that the proposed mechanism provides an incentive for the secondary users to participate in joint sensing at an extent depending on the signal-to-noise ratio and the reporting overhead.

This paper considers the cooperative optimization of mutual information in the MIMO Gaussian interference channel in a fully distributed manner via game theory. Null shaping constraints are enforced in the design of transmit covariance matrices to enable interference mitigation among links. The transmit covariance matrices leading to the Nash Equilibrium (NE) are derived, and the existence and uniqueness of the NE is analyzed. The formation of the cooperative sets, that represent the cooperation relationship among links, is considered as coalition games and network formation games. We prove that the proposed coalition formation (CF) and coalition graph formation (CGF) algorithms are Nash-stable, and the proposed network formation (NF) algorithm converges to a Nash Equilibrium. Simulation results show that the proposed CF and CGF algorithms have significant advantages when the antennas at the transmitters is large, and the proposed NF algorithm enhances the sum rate of the system apparently even at low signal-to-noise ratio region and/or with small number of transmit antennas.

In game theory, a cooperative game (or coalitional game) is a game with competition between groups of players (coalitions) due to the possibility of external enforcement of cooperative behavior (e.g. through contract law). Those are opposed to non-cooperative games in which there is either no possibility to forge alliances or all agreements need to be self-enforcing (e.g. through credible threats). Cooperative games are often analyzed through the framework of cooperative game theory, which focuses on predicting which coalitions will form, the joint actions that groups take and the resulting collective payoffs. It is opposed to the traditional non-cooperative game theory which focuses on predicting individual players’ actions and payoffs and analyzing Nash equilibriums. In this work, the cooperative and non-cooperative game problem is modeled by means of a modal logic formula. Then, using the concept of logic implication, and transforming this logical implication relation into a set of clauses, a modal resolution qualitative method for verification (satisfiability) as well as performance issues, for some queries is applied.

Purpose This paper aims to present various three-level service contracts among the following three participants: a manufacturer, an agent and a customer. The interaction between the aforementioned participants will be modeled using the game theory approach. Under non-cooperative and cooperative games, the optimal sale price, warranty period and warranty price for the manufacturer and the optimal maintenance cost (repair cost) and marketing expenditure for the agent are obtained by maximizing their profits. The satisfaction of the customer is also maximized by being able to choose one of the suggested options from the manufacturer and the agent, based on the risk parameter. Design/methodology/approach Three-echelon supply chains with marketing and warranty services are studied. Game-theoretic approaches (non-cooperative and cooperative) are presented. The non-cooperative approaches are static (NE) and dynamic (Stakelberg) models. The cooperative approach is related to bargaining models (Nash bargaining games). The authors develop a sensitivity analysis of some parameters and their effect. Findings Based on the mentioned drawbacks (i.e. lack of a model containing warranty, marketing and pricing), despite their importance, a developed model is proposed in this research to cover one of the research gaps. In addition, main contributions of this paper that differentiate it from the existing papers are regarding inventory, lost sale and lost goodwill, which are significant in the comparison environment. Another advantage of this study is related to the solution approach, the game theory. Twofold of the games theoretical, i.e. cooperative (in three forms) and non-cooperative are considered, because of their importance. Three types of non-cooperative games are presented as follows: Nash equilibrium – each echelon decides respectively and simultaneously; manufacturer-Stackelberg – the manufacturer has more power than the agent and the agent has more power than the customer; and customer-Stackelberg – customer is leader of the agent and the agent is the leader of manufacturer. The involved cooperative game in this paper is the bargaining problem that the participants can determine how to share the additional profits. Originality/value In this paper, various three-level service contracts will be presented among the following three participants: a manufacturer, an agent and a customer. The interaction between the aforementioned participants will be modeled using the game theory approach. Under non-cooperative and cooperative games, the optimal sale price, warranty period and warranty price for the manufacturer and the optimal maintenance cost (repair cost) and marketing expenditure for the agent are obtained by maximizing their profits. The satisfaction of the customer is also maximized by being able to choose one of the suggested options from the manufacturer and the agent, based on the risk parameter. Several numerical examples are used to illustrate the models presented in this paper. Finally, the authors develop a sensitivity analysis of some parameters and their effects on the objective functions.

SummaryWe consider fog computing scenarios where data generated by a set of IoT applications need to be processed locally by a set of fog nodes, belonging to distinct Fog Infrastructure Providers (FIPs) sharing the same co‐location facility, with the aim of increasing their profits. This is a challenging goal as it requires reducing costs and meeting QoS targets despite time‐varying workloads. We argue that these FIPs may find it profitable to cooperate by mutually sharing their workload and resources, and we show (by using a game‐theoretical framework) that this is indeed the case when stable coalitions can be formed. Based on these results, in this paper, we present (1) a mathematical model for maximizing the profit obtained for allocating IoT applications to a group of FIPs, and (2) a coalition formation algorithm that allows each FIP to decide with whom to cooperate so as to increment its profits. The efficacy of the devised algorithm is assessed by means of an experimental evaluation taking into account different workload intensities. The results from these experiments show the capability of the proposed algorithm to form coalitions of FIPs that are profitable and stable in all the scenarios we take into consideration.

In this paper, the problem of random access contention between machine type devices (MTDs) in the uplink of a wireless cellular network is studied. In particular, the possibility of forming cooperative groups to coordinate the MTDs’ requests for the random access channel (RACH) is analyzed. The problem is formulated as a stochastic coalition formation game in which the MTDs are the players that seek to form cooperative coalitions to optimize a utility function that captures each MTD’s energy consumption and time-varying queue length. Within each coalition, an MTD acts as a coalition head that sends the access requests of the coalition members over the RACH. One key feature of this game is its ability to cope with stochastic environments in which the arrival requests of MTDs and the packet success rate over RACH are dynamically time-varying. The proposed stochastic coalitional game is composed of multiple stages, each of which corresponds to a coalitional game in stochastic characteristic form that is played by the MTDs at each time step. To solve this game, a novel distributed coalition formation algorithm is proposed and shown to converge to a stable MTD partition. Simulation results show that, on the average, the proposed stochastic coalition formation algorithm can reduce the average fail ratio and energy consumption of up to 36% and 31% for a cluster-based distribution of MTDs, respectively, compared with a noncooperative case. Moreover, when the MTDs are more sensitive to the energy consumption (queue length), the coalitions’ size will increase (decrease).

Recently, significant attention has been paid to the controllability problem of networked systems. In many real-world multi-agent systems, agents are spatially located in arbitrary positions and can only communicate with nearby agents. A natural problem of designing such a system is how to determine the necessary communication radius that ensures a certain degree of controllability. Taking advantage of the recent findings on network controllability, we obtain theoretical results that aid in choosing such a radius. We find that the critical communication radius, with which one can use only a negligible number of driver nodes to control the whole system, is proportional to the inverse of the square root of node density. Finally, we present how our conclusions apply to systems of different scales, ranging from very-large scale systems that have nearly infinite nodes to ones with only a limited number of nodes. The probability for the calculated critical communication radius to have a certain error is shown to be bounded.

Second-order consensus of multi-agent systems with nonlinear dynamics via impulsive control

In large-scale, complicated Wireless Sensor Networks, the cooperation among sensor nodes is a key topic, and has been receiving more and more attention. The dynamic coalition mechanism in Multi-Agent System is an important method for this topic, and then an energy-efficient coalition formation algorithm is needed since the energy resource of WSN is restricted. This paper proposes a WSN coalition formation algorithm based on Ant Colony System with dual-negative feedback characteristic. Coalition sponsor inclines to wake those sensor nodes which seldom or never joined coalitions before to form coalition, which can balance the energy consumption among sensor nodes, so as to extend the network lifetime for more tasks. The results of simulation experiment show the validity of this algorithm.

In this paper we implemented a fully knowledge base approach for determine the attack/defense strategies of a team of soccer robots as a multi agent system which are playing two cooperative and non-cooperative games in a battle field simultaneously. First two teams play in non-cooperative game and find their equilibrium point and payoff then all players in a team tries reaching to this position. As there are several decision about team strategy in non-cooperative game and player strategy in cooperative game, our novel approach to use Fuzzy Integral Operators (F10) to fuse sensory information enables designer to softly change team strategy and play the most likelihood game to a real game. Selecting the shape of FIO affect directly on the team strategy and players' position and be reviewed in this paper. This approach will be imported in both, cooperative and non-cooperative game when we face to a multi- hypothesis structure. Results justified successfully the improved performance in team action.

By a cooperative game in coalitional structure or shortly coalitional game we mean the standard cooperative non-transferable utility game described by a set of payoffs for each coalition being a nonempty subset of the grand coalition of all players. It is well-known that balancedness is a sufficient condition for the nonemptiness of the core of such a cooperative non-transferable utility game. In this paper we consider non-transferable utility games in which for any coalition the set of payoffs depends on a permutation or ordering upon any partition of the coalition into subcoalitions. We call such a game a cooperative game in permutational structure or shortly permutational game. Doing so we extend the scope of the standard cooperative game theory in dealing with economic or political problems. Next we define the concept of core for such games. By introducing balancedness for ordered partitions of coalitions, we prove the nonemptiness of the core of a balanced non-transferable utility permutational game. Moreover we show that the core of a permutational game coincides with the core of an induced game in coalitional structure, but that balancedness of the permutational game need not imply balancedness of the corresponding coalitional game. This leads to a weakening of the conditions for the existence of a nonempty core of a game in coalitional structure, induced by a game in permutational structure. Furthermore, we refine the concept of core for the class of permutational games. We call this refinement the balanced-core of the game and show that the balanced-core of a balanced permutational game is a nonempty subset of the core. The proof of the nonemptiness of the core of a permutational game is based on a new intersection theorem on the unit simplex, which generalizes the well-known intersection theorem of Shapley.

A unified framework for reinforcement learning, co-learning and meta-learning how to coordinate in collaborative multi-agent systems

We present a bottom up approach, based on intelligent software agents, to real-time scheduling problems. The approach is market based; the schedule is arrived at through bidding. We recast the real-time scheduling problem as one of allocating, within stipulated deadlines, producer outputs to consumer demands over a time horizon. Producer agents submit bids to consumer agents. These bids are structured as option bids wherein consumer agents pay option premiums to producer agents thereby gaining the ability to postpone commitments. Producer agents use a game theory philosophy called coopetition; that is, they simultaneously compete and cooperate with other producer agents. When they compete, agents use the maximin principle from non-cooperative game theory to devise bidding strategies. When they seek to identify potential partners to coordinate bidding strategies with, agents use the Nash bargaining protocol from cooperative game theory. The framework has a strong positive feedback component in that success breeds success and only the fittest producer agents survive. Agents use memory based reasoning techniques to learn to revise their strategies as games are repeated. Agents are mobile; this enables them to conduct negotiations more efficiently by co-locating to the same machine. Agents are also endowed with limited speech recognition and speech synthesis capabilities; this facilitates interactions with the human decision maker who supervises the entire scheduling process.