In this paper, we consider distributed decision-making over directed time-varying multi-agent systems. We consider an adversarial bandit problem in which a group of agents chooses an option from among multiple arms to maximize the total reward. In the proposed method, each agent cooperatively searches for the optimal arm with the highest reward by a consensus-based distributed Exp3 policy. To this end, each agent exchanges the estimation of the reward of each arm and the weight for exploitation with the nearby agents on the network. To unify the explored information of arms, each agent mixes the estimation and the weight of the nearby agents with their own values by a consensus dynamics. Then, each agent updates the probability distribution of arms by combining the Hedge algorithm and the uniform search. We show that the sublinearity of a pseudo-regret can be achieved by appropriately setting the parameters of the distributed Exp3 policy.

Text-based communication, such as text chat, is commonly employed in various contexts, both professional and personal. However, it lacks the rich emotional cues present in verbal and visual forms of communication, such as facial expressions and tone of voice, making it more challenging to convey emotions and increasing the likelihood of misunderstandings. In this study, we focused on typefaces as emotional cues employed in text-based communication and investigated the influence of font design on impression formation and decision-making through two experiments. The results of the experiments revealed the relationship between Japanese typeface design and impression formation, and indicated that advice presented in a font evoking an impression of high confidence was more likely to be accepted than advice presented in a font evoking an impression of low confidence.

Online social networks have increased their impact on the real world, which motivates information senders to control the propagation process of information to promote particular actions of online users. However, the existing works on information provisioning seem to oversimplify the users' decision-making process that involves information reception, internal actions of social networks, and external actions of social networks. In particular, characterizing the best practices of information provisioning that promotes the users' external actions is a complex task due to the complexity of the propagation process in OSNs, even when the variation of information is limited. Therefore, we propose a new information diffusion model that distinguishes user behaviors inside and outside of OSNs, and formulate an optimization problem to maximize the number of users who take the external actions by providing information over multiple rounds. Also, we define a robust provisioning policy for the problem, which selects a message sequence to maximize the expected number of desired users under the probabilistic uncertainty of OSN settings. Our experiment results infer that there could exist an information provisioning policy that achieves nearly-optimal solutions in different types of OSNs. Furthermore, we empirically demonstrate that the proposed robust policy can be such a universally optimal solution.

To maintain blockchain-based services with ensuring its security, it is an important issue how to decide a mining reward so that the number of miners participating in the mining increases. We propose a dynamical model of decision-making for miners using an evolutionary game approach and analyze the stability of equilibrium points of the proposed model. The proposed model is described by the 1st-order differential equation. So, it is simple but its theoretical analysis gives an insight into the characteristics of the decision-making. Through the analysis of the equilibrium points, we show the transcritical bifurcations and hysteresis phenomena of the equilibrium points. We also design a controller that determines the mining reward based on the number of participating miners to stabilize the state where all miners participate in the mining. Numerical simulation shows that there is a trade-off in the choice of the design parameters.

Autonomous vehicles and advanced driver assistant systems (ADAS) are receiving notable attention as research fields in both academia and private industry. Some decision-making systems use sets of logical rules to map knowledge of the ego-vehicle and its environment into actions the ego-vehicle should take. However, such rulesets can be difficult to create — for example by manually writing them — due to the complexity of traffic as an operating environment. Furthermore, the building blocks of the rules must be defined. One common solution to this is using an ontology specifically aimed at describing traffic concepts and their hierarchy. These ontologies must have a certain expressive power to enable construction of useful rules. We propose a process of generating sets of explanatory rules for ADAS applications from data using ontology as a base vocabulary and present a ruleset generated as a result of our experiments that is correct for the scope of the experiment.

Player detection is an important part in sports video analysis. Over the past few years, several learning based detection methods using various supervised two-class techniques have been presented. Although satisfactory results can be obtained, a lot of manual labor is needed to construct the training set. To overcome this drawback, this letter proposes a player detection method based on one-class SVM (OCSVM) using automatically generated training data. The proposed method is evaluated using several video clips captured from World Cup 2010, and experimental results show that our approach achieves a high detection rate while keeping the training set construction's cost low.

In this paper, we present a coexistence protocol design for a coexistence information service to provide coexistence solutions among dissimilar or independently operated autonomous decision-making networks in a wireless communication environment over, specifically but not limited to, TV white space (TVWS) frequency bands. The designed coexistence protocol for the coexistence information service has three main functionalities: (1) To collect basic information of subscribed TVWS networks; (2) To support generating neighbor lists for the TVWS networks based on the geography information and/or propagation parameters; (3) To provide necessary information for TVWS networks to make coexistence decisions. Both theoretical analysis and simulation results show that the designed coexistence information service ensures harmonious communications among dissimilar networks and is able to achieve coexistence over an area with the limited number of available channels in white space.

This paper presents an adaptive maximum power point tracking (MPPT) algorithm. The aim is to dynamically adjust the step length for updating duty ratio (or operating voltage) so as to make full utilization of the output power of photovoltaic (PV) systems, even under the rapidly changing atmospheric conditions. To this end, the average slope in terms of voltage and power is exploited for reducing the harmful effect of noise and error (incurred in measurement or quantization) on the slope. Also, a statistical decision-making scheme is employed for reliably deciding the time instant at which atmospheric conditions actually change. Empirical study has adduced grounds for its dominance over existing references.

This paper investigates the autonomous decision-making process of the selection of alternative countermeasures against threats in electronic warfare settings. We introduce a threat model, which represents a specific threat pattern, and a methodology that decides the best countermeasure against real-time threats using the decision theory. To determine the optimal countermeasure, we model the probabilities of the effects of countermeasures, if executed, and combine the probabilities with their utilities. This methodology based upon the inductive threat model calculates the expected utilities of countermeasures which are applicable given a situation, and provide an intelligent command and control agent with the best countermeasure to threats. We present empirical results that demonstrate the agent's capabilities of choosing countermeasures to threats in simulated electronic warfare settings.

To examine the function of the superior colliculus (SC) in decision-making processes and the application of its single trial activity for "neural mind reading," we recorded from SC deep layers while two monkeys performed oculomotor go/no-go tasks. We have recently focused on monitoring single trial activities in single SC neurons, and designed a virtual decision function (VDF) to provide a good estimation of single-dimensional decisions (go/no-go decisions for a cue presented at a specific visual field, a response field of each neuron). In this study, we used two VDFs for multidimensional decisions (go/no-go decisions at two cue locations) with the ensemble activity which was simultaneously recorded from a small group (4 to 6) of neurons at both sides of the SC. VDFs predicted cue locations as well as go/no-go decisions. These results suggest that monitoring of ensemble SC activity had sufficient capacity to predict multidimensional decisions on a trial-by-trial basis, which is an ideal candidate to serve for cognitive brain-machine interfaces (BMI) such as two-dimensional word spellers.

There is increasing evidence from in vivo recordings in monkeys trained to respond to stimuli by making left- or rightward eye movements, that firing rates in certain groups of neurons in oculo-motor areas mimic drift-diffusion processes, rising to a (fixed) threshold prior to movement initiation. This supplements earlier observations of psychologists, that human reaction-time and error-rate data can be fitted by random walk and diffusion models, and has renewed interest in optimal decision-making ideas from information theory and statistical decision theory as a clue to neural mechanisms. We review results from decision theory and stochastic ordinary differential equations, and show how they may be extended and applied to derive explicit parameter dependencies in optimal performance that may be tested on human and animal subjects. We then briefly describe a biophysically-based model of a pool of neurons in locus coeruleus, a brainstem nucleus implicated in widespread norepinephrine release. This neurotransmitter can effect transient gain changes in cortical circuits of the type that the abstract drift-diffusion analysis requires. We also describe how optimal gain schedules can be computed in the presence of time-varying noisy signals. We argue that a rational account of how neural spikes give rise to simple behaviors is beginning to emerge.

This paper proposes a method of supporting behavioral decision-making with a rule-based system to be used for the design stage of service specifications or service scenarios based on a state transition model. We consider telecommunication services as service scenarios of state transitions. A state transition can be described as a transition rule that is represented as the notation of an IF-THEN rule : "if < condition > then < action >. " Thus, behaviors or service scenarios are assumed to be treated as a set of IF-THEN rules in this paper. In general, an "if < condition > then < action >" rule is called a production rule, and production rules are often used to represent knowledge in expert systems. Thus, rules treated in this paper are a kind of production rule. It is still difficult to decide if behaviors are concurrent, cooperative, or exclusive as a whole system when many service scenarios are combined, even if each rule is clearly described and easy to understand. When several service scenarios are combined, it is necessary for the sub-elements of each state transition to cooperate. The method proposed in this paper consists of the following techniques: (1) decompositions of a state transition to sub-elements, (2) rule descriptions for the decomposed sub-elements and the application of rules, (3) use of conflict and cooperation strategies, and (4) support for behavioral decision-making by showing recommendations for cooperative, exclusive, or concurrent behavior. We provide examples of telecommunication services to show the effectiveness of the proposed method in an easy-to-understand manner. We also show cooperative resolution in the interactions among several services.

Asynchronous, distributed, decision-making (ADDM) systems constitute a special class of distributed problems and are characterized as large, complex systems wherein the principal elements are the geographically-dispersed entities that communicate among themselves, asynchronously, through message passing and are permitted autonomy in local decision-making. A fundamental property of ADDM systems is stability that refers to their behavior under representative perturbations to their operating environments, given that such systems are intended to be real, complex, and to some extent, mission critical systems, and are subject to unexpected changes in their operating conditions. ADDM systems are closely related to autonomous decentralized systems (ADS) in the principal elements, the difference being that the characteristics and boundaries of ADDM systems are defined rigorously. This paper introduces the concept of stability in ADDM systems and proposes an intuitive yet practical and usable definition that is inspired by those used in Control Systems and Physics. A comprehensive stability analysis on an accurate simulation model will provide the necessary assurance, with a high level of confidence, that the system will perform adequately. An ADDM system is defined as a stable system if it returns to a steady-state in finite time, following perturbation, provided that it is initiated in a steady-state. Equilibrium or steady-state is defined through placing bounds on the measured error in the system. Where the final steady-state is equivalent to the initial one, a system is referred to as strongly stable. If the final steady-state is potentially worse then the initial one, a system is deemed marginally stable. When a system fails to return to steady-state following the perturbation, it is unstable. The perturbations are classified as either changes in the input pattern or changes in one or more environmental characteristics of the system such as hardware failures. Thus, the key elements in the study of stability include steady-state, perturbations, and stability. Since the development of rigorous analytical models for most ADDM systems is difficult, if not impossible, the definitions of the key elements, proposed in this paper, constitute a general framework to investigate stability. For a given ADDM system, the definitions are based on the performance indices that must be judiciously identified by the system architect and are likely to be unique. While a comprehensive study of all possible perturbations is too complex and time consuming, this paper focuses on a key subset of perturbations that are important and are likely to occur with greater frequency. To facilitate the understanding of stability in representative real-world systems, this paper reports the analysis of two basic manifestations of ADDM systems that have been reported in the literature --(i) a decentralized military command and control problem, MFAD, and (ii) a novel distributed algorithm with soft reservation for efficient scheduling and congestion mitigation in railway networks, RYNSORD. Stability analysis of MFAD and RYNSORD yields key stable and unstable conditions.