Masashi Hayano

Self-Organizational Reciprocal Agents for Conflict Avoidance in Allocation Problems

We propose reciprocal agents that self-organize associations based on cooperative relationships for efficient task/resource allocation problems in large-scale multi-agent systems (MASs). Computerized services are often provided by teams of networked intelligent agents by executing the corresponding tasks. However, performance in large-scale and busy MASs, may severely degrade due to conflicts because many task requests are excessively sent to a few agents with high capabilities. We introduce a game of N-agent team formation (TF game), which is an abstract form of the distributed allocation problem. We then introduce reciprocal agents that identifies dependable/trustworthy agents in TF games, shares the states between them, and preferentially works with them. Through this behavior with learning, they autonomously organize implicit associations that can considerably reduce conflicts and achieve fair reward distributions. We experimentally found that reciprocal agents could identify mutually dependable agents that formed independent associations, and efficiently team formed games. Finally, we investigated reasons for such efficient behaviors and found how their organizational structures emerged.

Analysis of task allocation based on social utility and incompatible individual preference

This paper proposes a task allocation method in which, although social utility is attempted to be maximized, agents also give weight to individual preferences based on their own specifications and capabilities. Due to the recent advances in computer and network technologies, many services can be provided by appropriately combining multiple types of information and different computational capabilities. The tasks that are carried out to perform these services are executed by allocating them to appropriate agents, which are computational entities having specific functionalities. However, these tasks are huge and appear simultaneously, and task allocation is thus a challenging issue since it is a combinatorial problem. The proposed method, which is based on our previous work, allocates resources/tasks to the appropriate agents by taking into account both social utility and individual preferences. We experimentally demonstrate that the appropriate strategy to decide the preference depends on the type of task and the features of the reward function as well as the social utility.

Adaptive Task Allocation Based on Social Utility and Individual Preference in Distributed Environments

Abstract Recent advances in computer and network technologies enable the provision of many services combining multiple types of information and different computational capabilities. The tasks for these services are executed by allocating them to appropriate collaborative agents, which are computational entities with specific functionality. However, the number of these tasks is huge, and these tasks appear simultaneously, and appropriate allocation strongly depends on the agent’s capability and the resource patterns required to complete tasks. Thus, we first propose a task allocation method in which, although the social utility for the shared and required performance is attempted to be maximized, agents also give weight to individual preferences based on their own specifications and capabilities. We also propose a learning method in which collaborative agents autonomously decide the preference adaptively in the dynamic environment. We experimentally demonstrate that the appropriate strategy to decide the preference depends on the type of task and the features of the task reward. We then show that agents using the proposed learning method adaptively decided their preference and could maintain excellent performance in a changing environment.

Switching Behavioral Strategies for Effective Team Formation by Autonomous Agent Organization

In this work, we propose agents that switch their behavioral strategy between rationality and reciprocity depending on their internal states to achieve efficient team formation. With the recent advances in computer science, mechanics, and electronics, there are an increasing number of applications with services/goals that are achieved by teams of different agents. To efficiently provide these services, the tasks to achieve a service must be allocated to agents that have the required capabilities and the agents must not be overloaded. Conventional distributed allocation methods often lead to conflicts in large and busy environments because high-capability agents are likely to be identified as the best team member by many agents, resulting in inefficiency of the entire system due to concentration of task allocation. Our proposed agents switch their strategies in accordance with their local evaluation to avoid conflicts occurring in busy environments. They also establish an organization in which a number of groups are autonomously generated in a bottom-up manner on the basis of dependability in order to avoid the conflict in advance while ignoring tasks allocated by undependable/unreliable agents. We experimentally evaluate our proposed method and analyze the structure of the organization that the agents established.

Formation of association structures based on reciprocity and their performance in allocation problems

We describe the reciprocal agents that build virtual associations in accordance with past cooperative work in a bottom-up manner and that allocate tasks or resources preferentially to agents in the same associations in busy large-scale distributed environments. Models of multiagent systems (MAS) are often used to express tasks that are done by teams of cooperative agents, so how each subtask is allocated to appropriate agents is a central issue. Particularly in busy environments where multiple tasks are requested simultaneously and continuously, simple allocation methods in self-interested agents result in conflicts, meaning that these methods attempt to allocate multiple tasks to one or a few capable agents. Thus, the systems performance degrades. To avoid such conflicts, we introduce reciprocal agents that cooperate with specific agents that have excellent mutual experience of cooperation. They then autonomously build associations in which they try to form teams for new incoming tasks. We introduce the N-agent team formation (TF) game, an abstract expression of allocating problems in MAS by eliminating unnecessary and complicated task and agent specifications, thereby identifying the fundamental mechanism to facilitate and maintain associations. We experimentally show that reciprocal agents can considerably improve performance by reducing the number of conflicts in N-agent TF games with different N values by establishing association structures. We also investigate how learning parameters to decide reciprocity affect association structures and which structure can achieve efficient allocations.

Asynchronous agent teams for collaborative tasks based on bottom-up alliance formation and adaptive behavioral strategies

This paper proposes a method to efficiently form teams for tasks that can be executed by multiple agents with different capabilities in a distributed network environment. Recent growing information and networking technologies have been realizing new types of computerized services that have been achieved by appropriately combining data from networked sensing devices and actuators controlled by intelligent programs in decentralized environments. Because these types of services can be realized by a team of agents acting using their own capabilities, how such teams can be formed effectively and efficiently in a distributed environment in a bottom-up manner is a key issue for autonomic computing. Our proposed method can autonomously recognize the dependable agents based on past successful cooperative behaviors, and they generate a tight alliance structure to execute the given tasks. Such an alliance structure avoids some conflicts by preventing many tasks being allocated to a few capable agents. We experimentally show that the proposed method can stably exhibit good performance and can adapt to environmental changes where task structure varies.

Adaptive Switching Behavioral Strategies for Effective Team Formation in Changing Environments

This paper proposes a control method for in agents by switching their behavioral strategy between rationality and reciprocity depending on their internal states to achieve efficient team formation. Advances in computer science, telecommunications, and electronic devices have led to proposals of a variety of services on the Internet that are achieved by teams of different agents. To provide these services efficiently, the tasks to achieve them must be allocated to appropriate agents that have the required capabilities, and the agents must not be overloaded. Furthermore, agents have to adapt to dynamic environments, especially to frequent changes in workload. Conventional decentralized allocation methods often lead to conflicts in large and busy environments because high-capability agents are likely to be identified as the best team member by many agents, resulting in the entire system becoming inefficient due to the concentration of task allocation when the workload becomes high. Our proposed agents switch their strategies in accordance with their local evaluation to avoid conflicts occurring in busy environments. They also establish an organization in which a number of groups are autonomously generated in a bottom-up manner on the basis of dependability to avoid conflicts in advance while ignoring tasks allocated by undependable/unreliable agents. We experimentally evaluated our method in static and dynamic environments where the number of tasks varied.

Balanced Team Formation for Tasks with Deadlines

A balanced team formation method is described for tasks with deadlines in multi-agent systems. With the advances that have been made in computer and network technologies, tasks that are achieved by multiple software/hardware entities are often required in many real-world applications. In addition, these tasks are usually required to be done by specified deadlines to avoid a failure of services or to provide quality services in a timely manner. We designed a method for effective team formation for cooperative work of different entities, called agents, to execute tasks having deadlines. The feature of our method is that rational agents autonomously learn which team they should join and which agents they should work with in order to improve the received rewards. Agents using the method also tried to select teams consisting of agents comparable with themselves, this can help them avoid binding to their teams unnecessarily. Another feature is that they estimate the duration of task execution to avoid a failure of tasks due to a violation of time requirements. We experimentally show that these three functions mutually affect each other positively and can achieve quite good performance in real-time environments.