Masato Noto

A method for the shortest path search by extended Dijkstra algorithm

The Dijkstra method is a well-known algorithm for finding the optimum path in shortest-path search problems. With that method, however, the time required to find the optimum path becomes remarkably long when the search scope is broad, so the Djikstra method is not suitable for real-time problems. In this paper, we propose a method for obtaining, in a short time, a path that is as close as possible to the path obtained by the Dijkstra method (the optimum path). The new method extends the conventional Dijkstra method so as to obtain a solution to a problem given within a specified time, such as path search in a car navigation system. The effectiveness of that extended method is described through use of simulations.

A word-of-mouth information recommender system considering information reliability and user preferences

In this paper, with the aim of facilitating information gathering on the Internet, we propose an information recommender system based on a word-of-mouth model that passes obtained information rapidly from one user to another while paying attention to information reliability and user preferences. The system uses a method for improving information reliability by treating user reliability and information reliability as equivalent and using an evaluation method employed on Internet auctions. It also uses a method that accommodates user preferences by changing the user that initiates communication (passes information) by using a personalized word-of-mouth agent that records user preferences. We report on very interesting phenomena revealed by our experimental results.

Quantitative evaluation of communication traffic of mobile agents in distributed constraint satisfaction model

A new software-structuring paradigm called the mobile agent technology is expected to be an important technology for reducing communication traffic from the software side. This technology features software objects called mobile agents. The use of a mobile agent can make communication delays very small by localizing the exchange of messages between two programs in one computer in cases that would otherwise require frequent communications between two computers. In this paper, we propose the use of mobile agents in a distributed constraint satisfaction model with the aim of reducing the load of communication traffic in distributed systems. We will present the empirical results obtained from simulations and discuss how agent mobility might affect overall performance in this model.

Empirical evaluation of distributed maximal constraint satisfaction method

A constraint satisfaction problem (CSP) is a general framework that can formalize various application problems in artificial intelligence. In this paper, we focus on an important subclass of distributed partial CSP called the distributed maximal CSP that can be applied to more practical kinds of problems. Specifically, we propose a method of solving distributed maximal CSPs using a combination of approximate and exact algorithms that yields faster optimal solutions than otherwise possible using conventional methods. Experimental results are presented that demonstrate the effectiveness of the proposed new approach.

Education and learning support system using proposed note-taking application.

Recently, development and offering of digital textbooks, educational materials, and educational platforms have begun in the field of education. In addition, with the rapid penetration of networks and proliferation of powerful digital devices capable of editing, some note-taking applications such as Note Anytime and OneNote are released by each company. These applications can create attractive handwritten notes on tablets and smartphones through an intuitive interface. Furthermore, linked to the cloud storage service, they can allow multiple users to edit documents together and share information.

A hybrid Particle Swarm Optimization considering accuracy and diversity of solutions

Particle Swarm Optimization (PSO) is an optimization method that emulates the behavior of creatures such as a flock of birds or a school of fish. Two typical PSO information exchange formats are the Gbest model and the Lbest model. The Gbest model is the most basic model, but this model can converge quickly on a solution and may become trapped at a local solution. On the other hand, the Lbest model converges slowly on the solution but its global search capability is better. In this study, we propose a method of remedying the drawback of PSO in that it tends to become trapped at a local solution, by maintaining the diversity of the search by a global search using the Lbest model in the early stages of the search, then switching to a local search by the Gbest model in the final stages. We also confirm the validity of this method by simulation experiments using benchmark problems. As a result, we confirmed that accuracy of discovery of the optimal solution was increased, although convergence on the solution was somewhat delayed.

The effect of education and learning using note-taking application

In recent years, in each stage of education from elementary to high school in Japan, the focus has been on teaching/learning programming. However, it cannot be said that all students are able to learn programming technology. Difficult problems exist in teaching programming because of differences in student achievement levels. These differences are found at the elementary and middle school levels of course, but are even more obvious at the high school level where courses begin to separate into humanities, science, and other fields.

Swarm intelligence algorithm for optimality discovery in distributed constraint optimization

The distributed constraint optimization problem (DCOP) is known as a basic problem of multiagent systems. This problem seeks optimal solutions using cooperative behavior between agents. However, because DCOP is NP-hard, the computational load becomes massive as the number of agents increase. What are needed are high-speed algorithms that can solve problems without increasing the computational load. In recent years, methods that intentionally limit the number of cooperative agents, such as k-optimality and p-optimality, have been proposed as approximate algorithms. They are drawing attention as new approximate algorithms because they can obtain deviations from the optimal solutions. The value of optimality is an artificially established partition of state space, and there is a need to change the value for different network topologies. In this paper, we propose a method that uses swarm intelligence as a new DCOP algorithm. Each agent evaluates the state space using swarm intelligence. The available state is determined not based on values decided by each agent. Instead, a cooperative space is produced based on heuristic values to determine the optimal solutions. We also show that a new criteria of optimality can be obtained using swarm intelligence.

Agent-based social simulation model that accommodates diversity of human values

People today exchange a great variety of information over the Internet. Much like the world at large, people bring a wide range of values to their interaction with each other as they communicate over the Internet. While the Internet has certainly facilitated social interaction, security measures have become absolutely essential to ensure private information is not compromised or tampering with. These security-related problems are also related to human values, but it is not easy to represent complex human values by conventional agent-based simulation. In this paper, we propose a model of information propagation in social networks using particle swarm optimization (PSO), a type of swarm intelligence algorithm, to simulate how different values affect human interaction. Simulations based on the model reveal that effects on propagation of information are quite different between environments where information is exchanged among agents with the same values and environments where information is exchanged among agents with different values.

An efficient flooding method in ad-hoc networks for reducing power consumption

In recent years, improved performance of wireless communication technology and lighter mobile terminals that have advanced functions have encouraged consideration of ad-hoc networks, which are temporary networks for the exchange of information among mobile wireless terminals. The features of ad-hoc networks include such advantages as not needing a network infrastructure and multi-hop communication. For practical use, however, the problem of terminal battery power consumption must be solved. An effective method for suppressing terminal power consumption is therefore necessary. The work reported here focuses on the battery power consumption caused by pre-communication flooding. We propose a flooding method that makes use of the characteristics of the existing DSR protocol to conserve power. We confirm the effectiveness of that method through simulation experiments. The experiments varied the cache data time to live and the parameters of the proposed method to evaluate power consumption and delay. The results show that the delay is increased somewhat, but the amount of power consumed is reduced.