Keiki Takadama

The University of Electro-Communications | UEC · Department of Informatics

This paper investigated the impacts of multi-objectivization on solving combinatorial single-objective NK-landscape problems with multiple funnel structures. Multi-objectivization re-formulates a single-objective target problem into a multi-objective problem with a helper problem to suppress the difficulty of the target problem. This paper analyzed...

This paper proposes the adaptive synapse adjustment and the adaptive decoding in the action-prediction cortical learning algorithm (ACLA) for an uncertain environment with probabilistically missing multiple input state values. The increase in the number of missing state values negatively affects the action prediction representation, and it is empha...

For an efficient upconvert of the Pareto front resolution by utilizing a known candidate solution set, this paper proposed an algorithm that built the Pareto front and the Pareto set estimation models and repeated to sample a solution from them, evaluate it, and updated the estimation models with it. Conventional supervised multi-objective optimiza...

This work introduces the following concepts of directional and estimated directional Pareto front to encourage multi-objective decision making, especially when the Pareto front exists in limited regions in the objective space. The general output of multi-objective optimization is a set of non-dominated solutions to approximate the Pareto front. Whe...

Air traffic flies from Asia to North America via the North Pacific (NOPAC) route system in oceanic airspace. The cruise altitudes of NOPAC routes are assigned on a first-come first-served basis. As a result, when an overflight and Japan departure flight compete for a cruise altitude, the former tends to receive its requested altitude, while the lat...

This paper focuses on the REM sleep estimation with bio-vibration data acquired from mattress sensor, and proposes its "correction" method based on Time-Series Confidence (TSC) of the REM sleep prediction calculated by Random Forest (RF) as one of the Machine Learnings (MLs). Unlike the conventional MLs that classify whether the REM sleep or not as...

It is important to detect daily Alzheimer dementia (AD) possibility using unconstrained mattress sensors because dementia takes time before subjective symptoms appear and the main treatment is to slow the rate of progression. Forcusing on circadian rhythm disorder which tend to occur with AD, this paper analyzes the features of unstable circadian r...

Social media is popular for us to share some news; however, it is easy for us to receive much fake news and believe them because of its simplicity. A new model is proposed for simulating a cyber-physical system preventing fake news with humans and agents by expanding the opinion sharing model (OSM), and this paper proposes a decision-supporting sys...

This paper proposes the clustering-based optimization method for landing sequence of aircraft, which partitions all the aircraft into several clusters and optimizes the schedules of these parted aircraft in parallel. We conducted the computer simulation of the Charles de Gaulle Airport in France and revealed that (1) our proposed method obtains the...

This paper focuses on the covering mechanism which generates a new if-then rule when the input data does not match the rules in the XCS Classifier System (XCS), a rule-based machine learning system, and discusses how the new rule should be generated from the viewpoint of “inheritance” and “expansion” of the generalization degree of the nearest neig...

The mission of this chapter is to formalize multi-objective reinforcement learning (MORL) problems where there are multiple conflicting objectives with unknown weights. The objective is to collect all Pareto optimal policies in order to adapt them for use in a learner's situation. However, it takes huge learning costs in previous methods, so this c...

To improve the accuracy to prevent from sharing incorrect opinion, this paper proposes a method which can share correct opinions based on majority decision for multi-opinion, named Gradient Descent Weight Tuning (GDWT). In the experiment, this paper compares GDWT with AAT and Self-information Weight Tuning (SWT) which weights the opinion from the a...

This paper proposes the novel Alzheimer dementia (AD) detection method based on unstable circadian rhythm of heartrate acquired from mattress sensor. Concretely, the pro-posed method, UCRADD (Unstable Circadian Rhythm based Alzheimer Dementia Detection), estimates the circadian rhythm of heartrate by calculating the regression of the trigonometric...

This paper proposes the novel Sleep Apnea Syndrome (SAS) detection method based on the frequency analysis of the overnight bio-vibration data acquired from mattress sensor. Concretely, this paper designs the index called Degree of Convexity of the Logarithmic Spectrum (DCLS), which quantifies the degree of convexity by computing the difference betw...

This paper establishes directionality reinforcement learning (DRL) technique to propose the complete decentralized multi-agent reinforcement learning method which can achieve cooperation based on each agent's learning: no communication and no observation. Concretely, DRL adds the direction "agents have to learn to reach the farthest goal among reac...

This paper focuses on the “early stage” of the online communication to investigate what kind of factors that contribute to forming a consensus among people who have their own way of thinking. For this purpose, this paper employs Barnga as the cross-cultural game where the players should select the winner according to their own rules, and analyzes o...

Reinforcement learning (RL) enables an agent to learn from trial-and-error experiences toward achieving long-term goals; automated planning aims to compute plans for accomplishing tasks using action knowledge. Despite their shared goal of completing complex tasks, the development of RL and automated planning has been largely isolated due to their d...

This paper proposes multi-factorial distance minimization problems for benchmarking of multi-factorial optimization. The multi-factorial optimization simultaneously searches for optimal solutions of multiple objective functions in the common variable space and is recently a popular issue regarding evolutionary optimization. The conventional multi-f...

This work proposes a method to estimate the Pareto front even in areas without objective vectors in the objective space. For the Pareto front approximation, we use a set of non-dominated points, objective vectors, in the objective space. To finely approximate the Pareto front, we need to increase the number of objective vectors. It is worth to esti...

This paper explores the key-factors that can promote people to form a consensus remotely in such an Internet environment, and designs the agent according to the found key-factor for a better online communication. To address this issue, this paper focuses on “declaration of intent” and regards that people form a consensus when sharing one thought wi...

This work proposes a multi-factorial evolutionary algorithm encouraging crossovers among solutions with similar target objective functions and suppressing crossovers among solutions with dissimilar target objective functions. Evolutionary multi-factorial optimization simultaneously optimizes multiple objective functions with a single population, a...

This work proposes a double-layered cortical learning algorithm. The cortical learning algorithm is a time-series prediction methodology inspired from the human neuro-cortex. The human neuro-cortex has a multi-layer structure, while the conventional cortical learning algorithm has a single layer structure. This work introduces a double-layered stru...

This paper reports a relationship between emotional expressions and consensus building in virtual communication. Concretely, we focus on emotions before consensus-building. To investigate the relationship, we employ Barnga which is one of card games for experiments. In Barnga, players cannot use language, and they have not the same rule. In additio...

This paper proposes the information sharing algorithm for preventing propagation of wrong information in the agent-based network such as SNS, and aims at investigating the effectiveness of the proposed algorithm through the complex network such as a small world network. Towards practical applications, this paper extends the conventional opinion sha...

This paper proposes a goal selection method to operate agents get maximum reward values per time by noncommunicative learning. In particular, that method aims to enable agents to cooperate along to dynamism of reward values and goal locations. Adaptation against to these dynamisms can enable agents to learn cooperative actions along to changing tra...

This paper proposes a novel master–slave parallel evolutionary algorithm (EA) approach with different asynchrony and provides its detailed analyses on multi-objective optimization problems. We express the proposed EA with different asynchrony as a semi-asynchronous EA. A semi-asynchronous EA generates new solutions whenever evaluations of the prede...

This paper proposes a method of adaptation for a problem holds local optima with different properties around them. We employed neighborhood based method into Adaptive DE:JADE with a mechanism of local adaptation simultaneously at different local optima. Experimental results revealed (i) local search is effective to multiple properties problem aroun...

This paper extended PMRL as the non-communicative and theoretical method for two agents, and proposed PLA as the method to be able to force agents to learn cooperative behavior for any number of agents. In addition, this paper adds the theoretic explanation for PLA that all agents achieve all purposes without spending the largest times. Concretely...

This chapter describes solving multi-objective reinforcement learning (MORL) problems where there are multiple conflicting objectives with unknown weights. Previous model-free MORL methods take large number of calculations to collect a Pareto optimal set for each V/Q-value vector. In contrast, model-based MORL can reduce such a calculation cost tha...

This paper proposes a multi-agent reinforcement learning method without communication toward dynamic environments, called profit minimizing reinforcement learning with oblivion of memory (PMRL-OM). PMRL-OM is extended from PMRL and defines a memory range that only utilizes the valuable information from the environment. Since agents do not require i...

For solving multi-objective optimization problems with evolutionary algorithms, the decomposing the Pareto front by using a set of weight vectors is a promising approach. Although an appropriate distribution of weight vectors depends on the Pareto front shape, the uniformly distributed weight vector set is generally employed since the shape is unkn...

To briefly represent a dataset, it is crucial to find common attributes among the data. Extended learning classifier system (XCS) finds common attributes of multiple data and acquires generalized rules that match multiple data. In real-world problems, it may be challenging to find common attributes due to noise in the data and the inability of XCS...

This paper focuses on Omoiyari in Japanese as consideration/thoughtfulness for others in order to promote people to obtain a consensus among them especially in Internet society where is difficult to reach a consensus due to the limited communication/interaction, and aims at exploring the preliminary agent design that can promote people to obtain a...

This paper describes solving multi-objective reinforcement learning problems where there are multiple conflicting objectives with unknown weights. Reinforcement learning (RL) is a popular algorithm for automatically solving sequential decision problems and most of them are focused on single-objective settings to decide a single solution. In multi-o...

In data mining, it is important to clarify how effective the acquired rules are and which elements are affected by rule evaluation. Extended learning classifier system (XCS) reveals factors that affect the classifier (rule) evaluation by generalizing the multiple classifiers that acquire the same reward (evaluation value) into a generalized classif...

In this paper, we proposed Bat Algorithm extending with Dynamic Niche Radius (DNRBA) which enables solutions to locate multiple local and global optima for solving multimodal optimization problems. This proposed algorithm is designed Bat Algorithm (BA) dealing with the exploration and the exploitation search with Niche Radius which is calculated by...

This paper focuses on the artificial bee colony (ABC) algorithm as one of swarm optimization methods and proposes ABC-alis (ABC algorithm based on adaptive local information sharing) by improving the ABC algorithm for dynamic optimization problems (DOPs). ABC-alis is applied to various types of dynamic changes embedded in DOPs to verify its trackin...

In mechatronics and robotics, one of the important issues is to design human interface. There are two issues on interaction design research. One is the way to education and training to adapt humans for operating the robots or interaction systems. Another one is the way to make interaction design adaptable for humans. This chapter research at the la...

In Artificial Intelligence and Robotics, one of the important issues is to design Human interface. There are two issues, one is the machine-centered interaction design to adapt humans for operating the robots or systems. Another one is the human-centered interaction design to make it adaptable for humans. This research aims at latter issue. This pa...

Toward learning cooperative behavior for any number of agents, this paper proposes a multi-agent reinforcement learning method without communication, called PMRL-based Learning for Any number of Agents (PLAA). PLAA prevents from agents reaching the purpose for spending too many times, and to promote the local multi-agent cooperation without communi...

In recent years, massive earthquakes struck Japan, causing large-scale disasters such as the great Hanshin-Awaji earthquake in 1997, the Niigata Prefecture Chuetsu earthquake in 2004, the great east Japan earthquake in 2011, and the Kumamoto earthquake in 2016. In the all disasters above, logistics system for relief supplies collapsed and it was re...

This paper focuses on the aircraft landing problem (ALP) and proposes an optimization method for ALP which addresses both the landing routes of multiple aircraft and their landing sequence. The difficulty of solving ALP is to optimize both the landing route and landing sequence of multiple aircraft, even the landing routes of the aircraft may be in...

This paper proposes high-dimensional data mining technique by integrating two data mining methods: Accuracy-based Learning Classifier Systems (XCS) and Random Forests (RF). Concretely the proposed system integrates RF and XCS: RF generates several numbers of decision trees, and XCS generalizes the rules converted from the decision trees. The conver...

We proposed XCS-VRc³ that can extract useful rules (classifiers) from data and verify its effectiveness. The difficulty of mining real world data is that not only the type of the input state but also the number of instances varies. Although conventional method XCS-VRc is able to extract classifiers, the generalization of classifiers was insufficien...

This paper proposes the novel Learning Classifier System (LCS) which can solve high-dimensional problems, and obtain human-readable knowledge by integrating deep neural networks as a compressor. In the proposed system named DCAXCSR, deep neural network called Deep Classification Autoencoder (DCA) compresses (encodes) input to lower dimension inform...

This paper focuses on Artificial Bee Colony (ABC) algorithm in dynamic optimization problems (DOPs), and proposes the improvements for ABC algorithm in DOPs as ABC algorithm based on adaptive local information sharing (ABC-alis). To investigate the tracking ability to dynamic change of ABC-alis, it is compared the improved algorithm to two cases of...

This paper proposes the multiple swarm optimization method composed of some numbers of populations, each of which is optimized by the different swarm optimization algorithm to adapt to dynamically change environment. To investigates the effectiveness of the proposed method, we apply it into the complex environment, where the objective function chan...

Accuracy based Learning Classifier System (XCS) prefers to generalize the classifiers that always acquire the same reward, because they make accurate reward predictions. However, real-world problems have noise, which means that classifiers may not receive the same reward even if they always take the correct action. For this case, since all classifi...

This paper introduces a reinforcement learning technique with an internal reward for a multi-agent cooperation task. The proposed methods is an extension of Q-learning which changes the ordinary (external) reward to the internal reward for agent-cooperation. Specifically, we propose here two Q-learning methods, both of which employ the internal rew...

This paper proposes a weighted opinion-sharing method called conformity-autonomous adaptive tuning (C-AAT) that enables agents to communicate and share correct information in a small-world network even when the links and information change dynamically. Concretely, each agent estimates weights for each of its neighbors by comparing their opinions wi...

This paper aims at achieving a stable high accuracy of opinion sharing in a distributed network with the agents which have initial opinions. Specifically, the network is composed of multi-agents, and most agents form their opinions according to the neighbors opinions which may be incorrect while a few agents only can receive outside information whi...

This paper proposes a learning goal space that visualizes the distribution of the obtained solutions to support the exploration of the learning goals for a learner. Subsequently, we examine the method for assisting a learner to present the novelty of the obtained solution. We conduct a learning experiment using the creative learning task to identif...

An action map is one of the most fundamental options in designing a learning classifier system (LCS), which defines how LCSs cover a state action space in a problem. It still remains unclear which action map can be adequate to solve which type of problem effectively, resulting in a lack of basic design methodology of LCS in terms of the action map....

This paper focuses on how to reduce the cognitive loads of air traffic controllers while solving the airport landing problem (ALP), which is the optimization of both aircraft landing routes and sequences. A method is proposed for adaptively changing landing sequences by optimization according to routes partially fixed by the controllers as a factor...

To increase the accuracy of real-time sleep stage estimation when only a small number of sleep data can be obtained such as after going to bed, the information for the subject's own sleep and other's past sleep can be used. However, the types of other sleep data such as the subject's own sleep data or other's sleep data, which can contribute to the...

This paper reports our experimental results on analyzing a human's goal finding process in continuous learning. The objective of this research is to make clear the mechanism of continuous learning. To fill in the missing piece of reinforcement learning framework for a learning robot, we focus on two human mental learning processes, awareness as pre...

This paper focuses on the Aircraft Landing Problem (ALP) and proposes the efficient aircraft landing route and order optimization method compared to the conventional method. As a difficulty in solving ALP, both landing route and order of all aircrafts should be optimized together, meaning that they cannot be optimized independently. To tackle this...

This paper introduces a reinforcement learning technique with an internal reward for a multi-agent cooperation task. The proposed method is an extension of Q-learning which changes the ordinary (external) reward to the internal reward for agent-cooperation under the condition of no communication. To increase the certainty of the proposed methods, w...

This study proposes a novel genetic programming method using asynchronous reference-based evaluation (called AREGP) to evolve computer programs through single-event upsets (SEUs) in the on-board computer in space missions. AREGP is an extension of Tierra-based asynchronous genetic programming (TAGP), which was proposed in our previous study. It is...

Contributing toward continuous planetary surface exploration by a rover (i.e., a space probe), this paper proposes (1) an adaptive learning mechanism as the software system, based on an exploration-biased genetic algorithm (EGA), which intends to explore several behaviors, and (2) a recovery system as the hardware system, which helps a rover exit s...

This paper focuses on the generalization of classifiers in noisy problems and aims at construction learning classifier system (LCS) that can acquire the optimal classifier subset by dynamically determining the classifier generalization criteria. In this paper, an accuracy-based LCS (XCS) that uses the mean of the reward (XCS-MR) is introduced, whic...

This study discusses important factors for zero communication, multi-agent cooperation by comparing different modified reinforcement learning methods. The two learning methods used for comparison were assigned different goal selections for multi-agent cooperation tasks. The first method is called Profit Minimizing Reinforcement Learning (PMRL); it...

The correctness rate of classification of neural networks is improved by deep learning, which is machine learning of neural networks, and its accuracy is higher than the human brain in some fields. This paper proposes the hybrid system of the neural network and the Learning Classifier System (LCS). LCS is evolutionary rule-based machine learning us...

This paper proposes a learning goal space that visualizes the distribution of the obtained solutions to support the exploration of the learning goals for a learner. Subsequently, we examine the method for assisting a learner to present the novelty of the obtained solution. We conduct a learning experiment using a continuous learning task to identif...

In this paper, we propose a method to improve ECS-DMR which enables appropriate output for imbalanced data sets. In order to control generalization of LCS in imbalanced data set, we propose a method of applying imbalance ratio of data set to a sigmoid function, and then, appropriately update the matching range. In comparison with our previous work...

This paper presents an approach to clustering that extends the variance-based Learning Classifier System (XCS-VR). In real world problems, the ability to combine similar rules is crucial in the knowledge discovery and data mining field. Conventionally, XCS-VR is able to acquire generalized rules, but it cannot further acquire more generalized rules...

This work proposes a decomposition-based multi-objective evolutionary algorithm utilizing variation angles among objective and weight vectors. The proposed algorithm introduces an angle-based proportional selection and dominance- and angle-based solution comparison criterion. Experimental results using WFG4 and WFG5 problems show that the proposed...