Modeling the Evolution of Agent Capabilities and Specialization

A social system is a patterned network of interrelationships that exist between individuals, institutions, and groups forming a coherent whole. Understanding the varying system outcomes for different decision-making processes selected under varying environment constraints in advance will aid in the realization the of best decision towards an effective outcome. One of the ways to increase system productivity is ‘Agent Specialization’. Also, the agents (individuals) who operate as generalists are most vulnerable to being replaced. Therefore, there is a need to focus on agent specialization to enhance the ability of an agent along with the evolution of an agent. Multi-Agent Based Simulation, a subfield of distributed AI, provides a technique to naturally describe a social system. To help improve decision-making intricacies of the agents to evolve and specialize, there is an increasing need to formulate an enhanced model of MABS. This thesis proposes a novel framework that exploits the benefits of social networks providing a decision support system for agent (individual) specialization by integrating the concept of ‘Positive Social Influence’ exerted by experts in the system. Consequently, the proposed framework assists the growth of agents by enabling the evolution of agent capabilities with the identification of suitable producer-agents using an evolutionary component (cultural algorithms). Enabling agent specialization and assisting the ability of the agents through capability evolution is anticipated to increase the productivity of the system. Evaluation of results shows the successful evolution of agent capabilities with the identification of suitable producer-agents in an optimized aspect (reduced operational cost and reduced distance cost) in comparison with exhaustive search, random search, and genetic algorithms and the improved degree of specialization of agents (increased dol values with a minimum of 3% increase to a maximum of 16.7% increase in comparison with standard genetic threshold model for varying agents and task number) in a given dynamic environment

Emergent Behavior Development and Control in Multi-Agent Systems

Emergence in natural systems is the development of complex behaviors that result from the aggregation of simple agent-to-agent and agent-to-environment interactions. Emergence research intersects with many disciplines such as physics, biology, and ecology and provides a theoretical framework for investigating how order appears to spontaneously arise in complex adaptive systems. In biological systems, emergent behaviors allow simple agents to collectively accomplish multiple tasks in highly dynamic environments; ensuring system survival. These systems all display similar properties: self-organized hierarchies, robustness, adaptability, and decentralized task execution. However, current algorithmic approaches merely present theoretical models without showing how these models actually create hierarchical, emergent systems. To fill this research gap, this dissertation presents an algorithm based on entropy and speciation - defined as morphological or physiological differences in a population - that results in hierarchical emergent phenomena in multi-agent systems. Results show that speciation creates system hierarchies composed of goal-aligned entities, i.e. niches. As niche actions aggregate into more complex behaviors, more levels emerge within the system hierarchy, eventually resulting in a system that can meet multiple tasks and is robust to environmental changes. Speciation provides a powerful tool for creating goal-aligned, decentralized systems that are inherently robust and adaptable, meeting the scalability demands of current, multi-agent system design. Results in base defense, k-n assignment, division of labor and resource competition experiments, show that speciated populations create hierarchical self-organized systems, meet multiple tasks and are more robust to environmental change than non-speciated populations

History-Based Response Threshold Model for Division of Labor in Multi-Agent Systems

Dynamic task allocation is a necessity in a group of robots. Each member should decide its own task such that it is most commensurate with its current state in the overall system. In this work, the response threshold model is applied to a dynamic foraging task. Each robot employs a task switching function based on the local task demand obtained from the surrounding environment, and no communication occurs between the robots. Each individual member has a constant-sized task demand history that reflects the global demand. In addition, it has response threshold values for all of the tasks and manages the task switching process depending on the stimuli of the task demands. The robot then determines the task to be executed to regulate the overall division of labor. This task selection induces a specialized tendency for performing a specific task and regulates the division of labor. In particular, maintaining a history of the task demands is very effective for the dynamic foraging task. Various experiments are performed using a simulation with multiple robots, and the results show that the proposed algorithm is more effective as compared to the conventional model