Role assignment is a critical task in role-based collaboration. It has three steps, i.e., agent evaluation, group role assignment, and role transfer, where group role assignment is a time-consuming process. This paper clarifies the group role assignment problem (GRAP), describes a general assignment problem (GAP), converts a GRAP to a GAP, proposes an efficient algorithm based on the Kuhn-Munkres (K-M) algorithm, conducts numerical experiments, and analyzes the solutions' performances. The results show that the proposed algorithm significantly improves the algorithm based on exhaustive search. The major contributions of this paper include formally defining the GRAPs, giving a general efficient solution for them, and expanding the application scope of the K-M algorithm. This paper offers an efficient enough solution based on the K-M algorithm that outperforms significantly the exhaustive search approach.

Group Role Assignment via a Kuhn–Munkres Algorithm-Based Solution

In cooperative multi-agent systems, roles are used as a design concept when creating large systems, they are known to facilitate specialization of agents, and they can help to reduce interference in multi-robot domains. The types of tasks that the agents are asked to solve and the communicative capabilities of the agents significantly affect the way roles are used in cooperative multi-agent systems. Along with a discussion of these issues about roles in multi-agent systems, this article compares computational models of the role allocation problem, presents the notion of explicitly versus implicitly defined roles, gives a survey of the methods used to approach role allocation problems, and concludes with a list of open research questions related to roles in multi-agent systems.

http://www.cs.ucf.edu/~ecl/papers/1004.jaamas.pdf

Multi-agent role allocation: issues, approaches, and multiple perspectives

We study a model of a competing population of N adaptive agents, with similar capabilities, repeatedly deciding whether to attend a bar with an arbitrary cutoff L. Decisions are based upon past outcomes. The agents are only told whether the actual attendance is above or below L. For L-> N/2, the game reproduces the main features of Challet and Zhang's minority game. As L is lowered, however, the mean attendances in different runs tend to divide into two groups. The corresponding standard deviations for these two groups are very different. This grouping effect results from the dynamical feedback governing the game's time-evolution, and is not reproduced if the agents are fed a random history.

Minority game with arbitrary cutoffs

Facing the increasing needs for large-scale, robust, adaptive, and distributed/decentralized computing capabilities from such fields as Web intelligence, scientific and social computing, Internet commerce, and pervasive computing, an unconventional bottom-up paradigm, based on the notions of Autonomy-Oriented Computing (AOC) and self-organization in open complex systems, offers new opportunities for developing promising architectures, methods, and technologies. The goal of this paper is to describe the key concepts in this computing paradigm, and furthermore, discuss some of the fundamental principles and mechanisms for obtaining self-organized computing solutions.

/pdf/autonomy-oriented-computing-aoc-the-nature-and-implications-yick9mb8lr.pdf

Autonomy-Oriented Computing (AOC): The Nature and Implications of a Paradigm for Self-Organized Computing

As research progresses, it enables multi-robot systems to be used in more and more complex and dynamic scenarios. Hence, the question arises how different modelling and reasoning paradigms can be utilised to describe the intended behaviour of a team and execute it in a robust and adaptive manner. Hendrik Skubch presents a solution, ALICA (A Language for Interactive Cooperative Agents) which combines modelling techniques drawn from different paradigms in an integrative fashion. Hierarchies of finite state machines are used to structure the behaviour of the team such that temporal and causal relationships can be expressed. Utility functions weigh different options against each other and assign agents to different tasks. Finally, non-linear constraint satisfaction and optimisation problems are integrated, allowing for complex cooperative behaviour to be specified in a concise, theoretically well-founded manner.

Modelling and Controlling of Behaviour for Autonomous Mobile Robots

In a team-based competitive game, agents cooperate to enhance their collective performance in winning the game. An interesting research problem in a team-based game is the role assignment problem (RAP). The problem requires agents to decide their respective roles based on real-time feedback from a dynamically changing environment. The minority game (MG), as used in modeling financial marketing problems, has shown similar characteristics that meet the fundamental requirements of RAP. We propose a formulation of MG strategies for studying RAP in a specific team-based game: RoboCup simulation league (RSL). Through experimentation, we demonstrate that MG strategies improve the effectiveness of role assignment among agents. The improvement validates some characteristics, e.g., the phase transition phenomenon on the memory size, as discovered in the theoretical MG model.

https://www.comp.hkbu.edu.hk/%7EAAMAS/papers/Ttwang-IAT04.pdf

Minority game strategies in dynamic multi-agent role assignment

A team-based competitive environment is a complex multi-agent environment, in which agents are required to coordinate among each other not only to enhance their collective behavior, but also to compete with other teams. Based on the minority game (MG) model, we have provided a strategy for assisting a team to win in RoboCup and in a more general environment, i.e., DynaGrid. In this paper, we aim to examine the effectiveness of the MG-based strategy for DynaGrid in more complete situations, e.g., both regular and irregular situations. We also propose a method for measuring the irregular complexity of a dynamic environment. Thus we are able to quantitatively figure out the typical situations in which the MG strategy works. Through experimental validation, we have found: (1) the MG strategy can generally speaking help a team of agents to enhance their competitiveness in a dynamically-changing environment, e.g., the target object is in a nonlinear or irregular motion; (2) the MG strategy does not have an edge over a commonly-used greedy strategy under some specific circumstances where a learning window is not large enough.

The minority game strategy in team competition: how and when?

A Hybrid Parallel Algorithm With Multiple Improved Strategies

Crow search algorithm is a kind of swarm intelligence optimization algorithm, which simulates the behavior of crows tracking each other. In order to improve the convergence accuracy of the algorithm and better balance the local and global search strategies, this paper proposes the crow search algorithm based on dynamic progressive strategy (DPCSA). The DPCSA uses a reverse search mechanism to improve the convergence speed, introduces the dynamic progressive strategy into the key parameters: awareness probability and flight length, adds the global optimal position information to the position updating dynamically. These improved strategies make the simulation of optimization process more reasonable. In order to evaluate the effectiveness of DPCSA, a series of test functions are used for experiments and the results show that the improved algorithm has good performance.

Tingting Wang

Papers

Minority game strategies in dynamic multi-agent role assignment

The minority game strategy in team competition: how and when?

A Hybrid Parallel Algorithm With Multiple Improved Strategies

Crow search algorithm with dynamic progressive strategy