Yoshitaka Sakurai

A Case Based Approach for an Intelligent Route Optimization Technology

This paper introduces a Case Based Approximation method to solve large scale Traveling Salesman Problems in a short time (around 3 seconds) with an error rate below 3%. This method is based on the insight, that a majority of real world problems are very often similar to previous ones at least for route scheduling. Thus, a solution can be derived from former solutions as follows: (1) selecting a most similar TSP from a library (CB: Case Base) of former TSP solutions, (2) removing the locations that are not including in the newly given problem or TSP and (3) adding the new locations by Nearest Insertion (NI) and possibly adjusting by NI incorporated GA. This way of creating solutions by Case Based Reasoning (CBR) avoids the computational costs to create new solutions from scratch. The evaluation of this method revealed remarkable results. Though even the world fastest most optimal approximate TSP solving method LKH needed more than 3 seconds or the worst error rate exceeded 3 seconds, the worst error rate of the proposed method is less than 1 % within 3 seconds. This is about 10-100 times better than that of our former approach BR-GA (Backtrack and Restart type GA).

Case based human oriented delivery route optimization

Delivery route optimization is a well-known NPcomplete problem based on the Traveling Salesman Problem (TSP) involving 20-2000 cities though human oriented factors make the problem more complex. Despite of NP-completeness, the scheduling should be solved every time within interactive response time and below expert level error or local optimality, considering human oriented factors including personal, social, and cultural factors. To cope with this, Cases and NI (Nearest Insertion) are introduced into a Genetic Algorithm (GA), based on the insight that real problems are similar to previous ones. A solution can be derived from former solutions, considering human oriented factors as follows: (1) retrieving the most similar cases, (2) modifying them by removing and adding locations by NI, and (3) further optimizing them by a GA using only NI operations. This cannot only diminish the costs to compute new solutions from scratch but also inherit many parts of previous routes to respect human factors. Experimental evaluation revealed remarkable results. Though the most effective TSP solving method LKH needed more than 3 seconds, the proposed method yielded results within 3% of the worst error rate and in less than 3 seconds. Furthermore, the proposed method is able to inherit most of the delivery routes, while LKH leads to significant changes.

Tweet credibility analysis evaluation by improving sentiment dictionary

To detect false information or rumors spread on Twitter on and after the Great East Japan Earthquake, a tweet credibility assessing method was proposed, based on the topic and opinion classification. The credibility is assessed by calculating the ratio of the same opinions to all opinions about a topic identified by topic models generated using Latent Dirichlet Allocation. To identify an opinion (positive or negative) about a tweet, sentiment analysis is performed using a semantic orientation dictionary. However, it is a kind of imbalanced data analysis to identify usually very few false tweets and the accuracy is a problem. The accuracy of the originally proposed method was susceptible since the sentiment opinion of most tweets was identified negative by the baseline (namely Takamuras) semantic orientation dictionary. To cope with this problem, a method for extracting sentiment orientations of words and phrases is also proposed to improve the evaluation for analyzing the credibility of tweet information. This method 1) evolutionally learns from a large amount of social data on Twitter, 2) focuses on adjective predicates, and 3) considers co-occurrences with negation expressions or multiple adjectives, between subjects and predicates, etc. The effects are proven by experiments using a large number of real tweets, in which we could detect rumor tweet much more accurately. In opposition to the baseline semantic dictionary, our method leads to succeed in imbalanced data analysis.

A case based approach for an intelligent route optimization technology

This paper introduces a Case Based Approximation method to solve large scale Traveling Salesman Problems in a short time (around 3 seconds) with an error rate below 3%. This method is based on the insight, that a majority of real world problems are very often similar to previous ones at least for route scheduling. Thus, a solution can be derived from former solutions as follows: (1) selecting a most similar TSP from a library (CB: Case Base) of former TSP solutions, (2) removing the locations that are not including in the newly given problem or TSP and (3) adding the new locations by Nearest Insertion (NI) and possibly adjusting by NI incorporated GA. This way of creating solutions by Case Based Reasoning (CBR) avoids the computational costs to create new solutions from scratch. The evaluation of this method revealed remarkable results. Though even the world fastest most optimal approximate TSP solving method LKH needed more than 3 seconds or the worst error rate exceeded 3 seconds, the worst error rate of the proposed method is less than 1 % within 3 seconds. This is about 10-100 times better than that of our former approach BR-GA (Backtrack and Restart type GA).

Distributed GAs with case-based initial populations for real-time solution of combinatorial problems

Combinatorial problems are NP-complete, which means even infinite number of CPUs take polynomial time to search an optimal solution. Therefore approximate search algorithms such as Genetic Algorithms are used. However, such an approximate search algorithm easily falls into local optimum and just distributed / parallel processing seems inefficient. In this paper, we introduce distributed GAs, which compute their initial population in a case-based manner and compose their upcoming generations by the particular GAs, which exchange their solutions and make their individual decisions, when composing a next generation based on the fitness of the candidates and diversity issues.

Knowledge Acquisition issues for intelligent route optimization by evolutionary computation

The paper introduces a Knowledge Acquisition and Maintenance concept for a Case Based Approximation method to solve large scale Traveling Salesman Problems in a short time (around 3 seconds) with an error rate below 3 %. This method is based on the insight, that most solutions are very similar to solutions that have been created before. Thus, in many cases a solution can be derived from former solutions by (1) selecting a most similar TSP from a library of former TSP solutions, (2) removing the locations that are not part of the current TSP and (3) adding the missing locations of the current TSP by mutation, namely Nearest Insertion (NI). This way of creating solutions by Case Based Reasoning (CBR) avoids the computational costs to create new solutions from scratch.

An Approach to Consider Diversity Issues from a Semantic Point of View

In this paper, we discuss a semantic and application-driven approach to estimate diversity respectively similarity in Genetic Algorithms (GA) based on a relative distance. This diversity metric can used to decide, whether or not a new individual meets a requested degree of diversity. Furthermore, the trade-off between several versions of the metric and their computational complexity is discussed. Finally, the application of this metric and a formerly developed Backtrack- and Restart GA to solve the Travelling Salesman Problem under certain real time requirements is introduced along with experimental evaluation.

Autonomous Distributed GA for Solving Real-Time Combinatorial Problems

Combinatorial problems are NP-complete, which means even infinite number of CPUs take polynomial time to search an optimal solution. Therefore approximate search algorithms such as Genetic Algorithms are used. However, such an approximate search algorithm easily falls into local optimum and just distributed / parallel processing seems inefficient. In this paper, this inefficiency is shown by simulation using TSP library as the example of optimal route scheduling. Then, an autonomous distributed GA to cope with this inefficiency through exchanging information about individuals (to calculate fitness /divergence /situation) among autonomous CPUs is proposed in solving real-time combinatorial problems. Using TSP library again, its effectiveness is shown by simulation experiments.

Intelligent Route Optimization Technology by Case Based GA

A delivery route optimization system greatly improves the real time delivery efficiency. To realize such an optimization, its distribution network requires solving several tens to hundreds (maximum 2 thousands or so) cities Traveling Salesman Problems (TSP) within interactive response time (around 3 seconds) with expert-level accuracy (below 3% level of error rate). To meet these requirements, a Case Based Genetic Algorithm (CBGA) is proposed. This method is based on the insight, that most solutions are very similar to solutions that have been created before. Thus, in many cases a solution can be derived from former solutions by (1) selecting a most similar TSP from a library of former TSP solutions, (2) removing the locations that are not part of the current TSP, (3) adding the missing locations of the current TSP by mutation, namely Nearest Insertion (NI), and (4) further optimizing the solution by another GA. This way of creating solutions by Case Based Reasoning (CBR) avoids the computational costs to create new solutions from scratch.

Context respectful counseling agent integrated with robot nodding for dialog promotion

Nowadays, a lot of IT personnel have psychological distress. Meanwhile, counselors to help them are lack in number. To solve the problem, we proposed a counseling agent (CA) called CRECA (context respectful counseling agent). CRECA listens to clients and promotes their reflection context respectfully namely in a context preserving way. This agent can be enhanced using a body language called unazuki in Japanese, a kind of nodding to greatly promote dialogue, often accompanying un-un (meaning exactly) of Japanese onomatopoeia. This body language is expected to significantly help represent empathy or entire approval. In this paper, the agent is integrated with such a unazuki or dialog promotion nodding robot to continue the conversation naturally or context respectfully towards clients further reflection. To realize such unazuki, the robot nods twice at each end of dialog sentence input by clients. The experimental evaluation proves such nodding is effective in counseling.