Behavior Selection Mechanism Research Articles

Twinning behavior and variant selection mechanism of b.c.c tantalum during dynamic plastic deformation

In this paper, the microstructure and twinning behavior of tantalum (Ta) during dynamic plastic deformation (DPD) were jointly analyzed in combination with impact experiments and molecular dynamics simulations, with a focus on the mechanism of variant selection for twinning. The results indicated that twins nucleate at grain boundaries and gradually grows with increasing deformation until it penetrates the entire grain. Due to the difference of grain boundary dislocation density, strain rate and deformation temperature have significant impacts on the quantity and distribution of twins. In addition, at room temperature, nearly half of the twin variants in DPD samples do not conform to Schmid law, and almost all the twin variants in liquid nitrogen samples do not follow to Schmid law. The activation of these non-Schmid variants is mainly affected by the strain coordination. Through this study, we have deepened our understanding of the dynamic microstructural response of Ta under high-speed deformation conditions.

Network evolutionary game analysis of green credit: A perspective of carbon emissions trading

AbstractAs an effective measure for the local government to promote reduction of corporate carbon emissions and sustainable development of social economy, the implementation of green finance policies in enterprises and financial institutions has encountered severe strikes from the low returns of low carbon production and low interest rates of green credit business. Under the premise of liberalizing carbon emissions trading, a triple‐level network evolutionary game consisting of a local government, banks, and enterprises is constructed to analyze response behavior and strategy selection mechanism to green credit. The impacts of management tools are investigated in terms of setting carbon market price, limiting the financing upper bound of misbehaving enterprises, limiting the emissions quota of enterprises for fraudulent green credit, and rewarding clean project development on the decisions of individuals in the network. Results show the following: First, increasing the trading price of carbon emissions has a catalytic effect on enhancing the activeness of banks and enterprises to follow green finance policies, but the autonomous regulation ability of which is limited. Second, reducing the funding ceiling for emitting emissions illegally ambiguates enterprises' preferences for production strategies and discourages them from undertaking low‐carbon projects. Third, excessive restrictions on the share of emissions may evoke regulatory inaction by the local government, consequently leading to instability in the evolution network. Fourth, granting reward for clean production is instrumental for the network to promote green finance policies, but exorbitant subsidies increase the fiscal burden of the local government with no significant effect on the node selection of environment‐friendly strategies.

Improved two-mode dynamic model for external-cavity quantum cascade lasers under strong optical feedback

In this paper, we propose an improved model for describing the dynamical behavior and mode selection mechanism of an external-cavity quantum-cascade laser (EC-QCL) when subject to strong optical feedback. Theoretical analysis including the dynamical effective reflectivity model, two-mode rate-equation model and Lang-Kobayashi equations are employed to carry out the laser output characteristics under the multiple feedback effects. A main aspect of the model is that the excess mirror loss caused by the EC effective reflectivity is incorporated into the laser dynamical equations as a nonlinear dynamical variable. Because of accounting the multiple round-trip feedback terms in the EC effective reflectivity, the analysis is valid for the arbitrary feedback regimes from weak to strong levels. Besides the stationary characteristics, the model accurately predicts the dynamical competition between the fundamental laser-cavity mode and the desired EC mode that reflected strongly by external diffraction grating. The results are in well agreement with theoretical and experimental data reported earlier for the EC-QCLs.

Optimal incentive contract in continuous time with different behavior relationships between agents

Dynamic contracts with multiple agents is a classical decentralized decision-making problem with asymmetric information, it is usually discussed according to moral hazard and the behavioral relationship between agents. To do so, in this paper, according to behavior relationships between agents, we analyze continuous time optimal contracting in principal multi-agent moral hazard settings. According to stochastic optimal control theory, the optimal contract of the generalized principal-agent dynamic problem is given, the optimal behavior selection and incentive mechanism of agents are analyzed. The result shows that, in the two-agent model, the incentive effect of cooperative relationship is greater than that of competitive relationship; when they are in a cooperative relationship, with the more influential agent receiving higher pay; under multi-agent model, an increase in the number of agents reduces effort and rewards, this indicates that the team size has strict boundaries. The research conclusions can be applied to solve two kinds of principal-agent problems that the principal needs to motivate the agent to compete or cooperate in the actual social production and life.

Emotion and memory model for social robots: a reinforcement learning based behaviour selection

ABSTRACT In this paper, we propose a reinforcement learning (RL) mechanism for social robots to select an action based on users’ learning performance and social engagement. We applied this behavior selection mechanism to extend the emotion and memory model, which allows a robot to create a memory account of the user’s emotional events and adapt its behavior based on the developed memory. We evaluated the model in a vocabulary-learning task at a school during a children’s game involving robot interaction to see if the model results in maintaining engagement and improving vocabulary learning across the four different interaction sessions. Generally, we observed positive findings based on child vocabulary learning and sustaining social engagement during all sessions. Compared to the trends of a previous study, we observed a higher level of social engagement across sessions in terms of the duration of the user gaze toward the robot. For vocabulary retention, we saw similar trends in general but also showing high vocabulary retention across some sessions. The findings indicate the benefits of applying RL techniques that have a reward system based on multi-modal user signals or cues.

Dynamical Behavior and Conformational Selection Mechanism of the Intrinsically Disordered Sic1 Kinase-Inhibitor Domain.

Intrinsically Disordered Peptides and Proteins (IDPs) in solution can span a broad range of conformations that often are hard to characterize by both experimental and computational methods. However, obtaining a significant representation of the conformational space is important to understand mechanisms underlying protein functions such as partner recognition. In this work, we investigated the behavior of the Sic1 Kinase-Inhibitor Domain (KID) in solution by Molecular Dynamics (MD) simulations. Our results point out that application of common descriptors of molecular shape such as Solvent Accessible Surface (SAS) area can lead to misleading outcomes. Instead, more appropriate molecular descriptors can be used to define 3D structures. In particular, we exploited Weighted Holistic Invariant Molecular (WHIM) descriptors to get a coarse-grained but accurate definition of the variegated Sic1 KID conformational ensemble. We found that Sic1 is able to form a variable amount of folded structures even in absence of partners. Among them, there were some conformations very close to the structure that Sic1 is supposed to assume in the binding with its physiological complexes. Therefore, our results support the hypothesis that this protein relies on the conformational selection mechanism to recognize the correct molecular partners.

A Framework of Multiagent Systems Behavioral Control Under State-Dependent Network Protocols

In this investigation, a unifying framework for designing distributed linear-like and nonlinear state-dependent protocols to control the behavior of the multiagent systems over communication networks is presented. Various behaviors of the connected agents, mainly on undirected graphs, are achieved by well-designed couplings where consensus, formation, and deployment are some examples of the achievable behaviors. Composite behaviors stored in a behavioral bank can be selected by a suitable behavior selection mechanism that is controlled directly by the agent embedded artificial intelligence or indirectly through a mission planning utility. The detailed structure of the framework is provided, where the integration of first integral and nonlinear eigenvalue approaches constitute its core is presented. The nonlinear protocols can be converted into their equivalent linear-like protocols by applying a simple transformation. However, the needed conditions will be the same in both cases. Consensus protocols-both linear like and nonlinear-to achieve arithmetic, geometric, and harmonic means are covered. A generalization using the mean-of-order-p is also provided. Stability and convergence issues are mainly handled using properties of M-matrices and Lasalle's principle. The use of state-dependent parameterization to control behaviors is presented. The multitude of primitive behaviors is used to build a more sophisticated behavioral bank that resides in each agent such that an agent can choose or follow the recently active behavior.

CASH only: Constitutive autonomy through motorsensory self-programming

Constitutive autonomy is the capacity of an entity to perpetually develop its individual constitution and coupling with its environment. We argue that computational entities (i.e., entities that can perform computation) can gain constitutive autonomy through motorsensory self-programming – a mechanism by which the entity acquires new computational processes as a series of patterns of interaction that the entity can learn through experience, simulate internally, and enact in the environment. Motorsensory self-programming allows the evolution of the cognitive coupling between the entity’s behavior selection mechanism and the environment as it appears from the viewpoint of the behavior selection mechanism. Constitutive autonomy of computational entities could lead to genuine agency.

The Government’s Goal of Poverty Alleviation and the Deviation of Peasants’ Behavior Choice: Theory and Empirical Evidence from Industrial Poverty Alleviation Projects

Under the strong constraint of the goal of eliminating poverty by 2020, the results induced by the acceleration of the government’s poverty alleviation policy may not be consistent with the original intention of poverty alleviation policy formulation. We incorporate the government intervention into the framework of peasants’ rational choice in industrial poverty alleviation projects. Our purpose is to analyze the reasons for the deviation between the government’s goal of poverty alleviation and peasants’ behavior choice in industrial poverty alleviation projects. Our empirical evidence suggests that: (1) It is conducive to improve peasants’ willingness and intensity of participation when the crop selection uses the inner marginal land. (2) Because of the difference in the endowment of peasants, the government’s compulsory in increasing the project participation rate will expose poor households to greater income risks, which will result in a bigger income gap. Poor households tend to choose the traditional operation with low risk and low expected income because of their low ability to resist risks. (3) Finally, the government’s compulsory intervention reduces peasants’ sensitivity to changes in price of the land rent and the labor market. Our main conclusions are: (1) Poverty alleviation policies should expect the behavior selection mechanism of poor households. (2) Government intervention in poverty alleviation activities faces a balance between reducing transaction costs and the loss of poverty alleviation efficiency. That is, if the intervention of local governments can be restrained in order to reduce the transaction cost of economic activities, it may be Pareto improvement of the efficiency of poverty alleviation policies. Compared with the existing research, the marginal contributions of this paper are as follows: (1) We discuss the boundary of government intervention in industrial poverty alleviation policies. (2) We clarify the impact mechanism of government intervention on peasants’ withdrawal behavior in industrial poverty alleviation projects. (3) It infers the cost of government intervention in the participation rate of industrial poverty alleviation projects. The government’s compulsory intervention may reduce peasants’ sensitivity to changes in factor compensation.

Overview on Emotion-Affected Behavior Selection Mechanism

The traditional Behavior Selection Mechanism (BSM) has two problems: it can’t be achieved autonomously under uncertainty conditions; its selection is absence of diversity under the same conditions. Being inspirited by the research achievements in emotion mechanism application to affective calculation and reinforcement learning, emotion is applied to BSM research to settle its problems. An emotion-affected mechanism is suggested. It is design-based totally; there are not the corresponding academic supports on it. Therefore, there exist three problems: the necessary elements, which it should have had, have not involved in; its architectures and emotion models are different; in addition, systematic analysis and comparisons have not been conducted among them. The suggestions are offered that the emotion-affected behavior selection mechanism should start off with mining the internal consistency among relevant research results of emotion in microeconomics, psychology, ethology, etc. Meanwhile, comparative research method will be meaningful.

Switching Mechanism of Sensor–Motor Coordination Through an Oscillator Network Model

Insects have small brains, but their behavior is highly adaptive; this leads us to conclude that their brains possess a simple adaptation mechanism. This paper focuses on the pheromone processing of crickets, varying their aggression depending on their global neural connection, and proposes a behavior selection mechanism that can be controlled by network transformation. The controller is composed of an oscillator network, and its behavior is decided by the synchrony of organic oscillations. Furthermore, every network component corresponds to a certain brain module. A model is realized by using an analog circuit, and it is applied to a simple robot that displays the behavior of a real insect.

A unified architecture for agent behaviors with selection of evolved neural network modules

To model complex systems for agent behaviors, genetic algorithms have been used to evolve neural networks which are based on cellular automata. These neural networks are popular tools in the artificial life community. This hybrid architecture aims at achieving synergy between the cellular automata and the powerful generalization capabilities of the neural networks. Evolutionary algorithms provide useful ways to learn about the structure of these neural networks, but the use of direct evolution in more difficult and complicated problems often fails to achieve satisfactory solutions. A more promising solution is to employ incremental evolution that reuses the solutions of easy tasks and applies these solutions to more difficult ones. Moreover, because the human brain can be divided into many behaviors with specific functionalities and because human beings can integrate these behaviors for high-level tasks, a biologically-inspired behavior selection mechanism is useful when combining these incrementally evolving basic behaviors. In this paper, an architecture based on cellular automata, neural networks, evolutionary algorithms, incremental evolution and a behavior selection mechanism is proposed to generate high-level behaviors for mobile robots. Experimental results with several simulations show the possibilities of the proposed architecture.

2P1-K08 感覚系情報処理と運動系情報処理の相互作用による行動選択機構

本研究は, ヒューマノイドの行動選択問題において, センサ情報を用いた単純な条件判断ではなく, 予想されるセンサ情報と実際のセンサ情報とのギャップを利用して行動の選択を行う。このアプローチにより, ロボットが自らの身体性に基づいた行動の選択基準を構築する事が可能となる。具体的には, 他者が行動する際の運動データとセンサデータを観測しリカレントニューラルネットによってその相互作用モデルを学習する。逐次的な相互作用を評価する手法を導入し, シミュレーション上のヒューマノイドでその有効性を確認した。