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An evolutionary behavioral model for decision making

机译：决策的进化行为模型

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For autonomous agents the problem of deciding what to do next becomes increasingly complex when acting in unpredictable and dynamic environments while pursuing multiple and possibly conflicting goals. One of the most relevant behavior-based models that tries to deal with this problem is the behavior network model proposed by Maes. This model proposes a set of behaviors as purposive perception-action units that are linked in a nonhierarchical network, and whose behavior selection process is orchestrated by spreading activation dynamics. In spite of being an adaptive model (in the sense of self-regulating its own behavior selection process), and despite the fact that several extensions have been proposed in order to improve the original model adaptability, there is not yet a robust model that can self-modify adap-tively both the topological structure and the functional purpose of the network as a result of the interaction between the agent and its environment Thus, this work proposes an innovative hybrid model driven by gene expression programming, which makes two main contributions: (I) given an initial set of meaningless and unconnected units, the evolutionary mechanism is able to build well-defined and robust behavior networks that are adapted and specialized to concrete internal agent's needs and goals; and (2) the same evolutionary mechanism is able to assemble quite complex structures such as deliberative plans (which operate in the long-term) and problem-solving strategies. As a result, several properties of self-organization and adaptability emerged when the proposed model was tested in a robotic environment using a multi-agent platform.
机译：对于自治代理，当在无法预测和动态的环境中行动并且追求多个甚至可能相互冲突的目标时，决定下一步做什么的问题变得越来越复杂。尝试解决此问题的最相关的基于行为的模型之一是Maes提出的行为网络模型。该模型提出了一组行为，将其作为有目的的感知-行为单元，这些行为在非分层网络中链接，并且其行为选择过程通过扩散激活动力学进行协调。尽管是自适应模型（从自我调节其自身的行为选择过程的意义上来说），并且尽管已经提出了若干扩展以提高原始模型的适应性的事实，但是仍然没有一种健壮的模型可以由于代理与其环境之间的相互作用，自适应地自我修改网络的拓扑结构和功能目的。因此，这项工作提出了一种由基因表达编程驱动的创新混合模型，该模型做出了两个主要贡献： （I）给出了初始的毫无意义的，没有联系的单元集，这种进化机制能够建立定义明确且健壮的行为网络，以适应和专门化具体内部代理商的需求和目标； （2）相同的进化机制能够组合相当复杂的结构，例如审议计划（长期运行）和解决问题的策略。结果，当在使用多智能体平台的机器人环境中测试所提出的模型时，出现了自组织和适应性的几种属性。

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