REPLANNING MECHANISM FOR DELIBERATIVE AGENTS IN DYNAMIC CHANGING ENVIRONMENTS

This paper proposes a replanning mechanism for deliberative agents as a new approach to tackling the frame problem. We propose a beliefs desires and intentions (BDI) agent architecture using a case-based planning (CBP) mechanism for reasoning. We discuss the characteristics of the problems faced with planning where constraint satisfaction problems (CSP) resources are limited and formulate, through variation techniques, a reasoning model agent to resolve them. The design of the agent proposed, named MRP-Ag (most-replanable agent), will be evaluated in different environments using a series of simulation experiments, comparing it with others such as E-Ag (Efficient Agent) and O-Ag (Optimum Agent). Last, the most important results will be summarized, and the notion of an adaptable agent will be introduced.     

协同设计中的多主体动态协调

确保多个主体协调一致的问题求解是协同设计系统性能好坏的重要标志。为此提出了一个面向黑板的混合推理主体结构。其中,基于事例推理实现多主体动态协调,用以解决由于主体间冲突所引起的系统控制问题。在考虑了已知事例属性个数以及与所求问题属性的相容数、匹配数的基础上,提出一事例匹配估算函数。     

一种适应于通信协议自稳定性的恢复算法

主要提出了一种适应于通信协议自稳定性的恢复算法 .先论述了适用于该算法的有关概念及形式描述方法 ;接着描述了该恢复算法 ,并给出了该算法的正确性证明及复杂性分析 ;最后通过一个多进程通信协议的实例研究 ,验证了该算法的正确性和有效性 .     

PROPOSITIONAL DYNAMIC LOGIC FOR REASONING ABOUT FIRST‐CLASS AGENT INTERACTION PROTOCOLS

For agents to fulfill their potential of being intelligent and adaptive, it is useful to model their interaction protocols as executable entities that can be referenced, inspected, composed, shared, and invoked between agents, all at runtime. We use the term first‐class protocol to refer to such protocols. Rather than having hard‐coded decision‐making mechanisms for choosing their next move, agents can inspect the protocol specification at runtime to do so, increasing their flexibility. In this article, we show that propositional dynamic logic (PDL) can be used to represent and reason about the outcomes of first‐class protocols. We define a proof system for PDL that permits reasoning about recursively defined protocols. The proof system is divided into two parts: one for reasoning about terminating protocols, and one for reasoning about nonterminating protocols. We prove that proofs about terminating protocols can be automated, while proofs about nonterminating protocols are unable to be automated in some cases. We prove that, for a restricted class of nonterminating protocols, proofs about them can be transformed to proofs about terminating protocols, making them automatable.     

GOAL‐DRIVEN AUTONOMY FOR RESPONDING TO UNEXPECTED EVENTS IN STRATEGY SIMULATIONS

To operate autonomously in complex environments, an agent must monitor its environment and determine how to respond to new situations. To be considered intelligent, an agent should select actions in pursuit of its goals, and adapt accordingly when its goals need revision. However, most agents assume that their goals are given to them; they cannot recognize when their goals should change. Thus, they have difficulty coping with the complex environments of strategy simulations that are continuous, partially observable, dynamic, and open with respect to new objects. To increase intelligent agent autonomy, we are investigating a conceptual model for goal reasoning called Goal‐Driven Autonomy (GDA), which allows agents to generate and reason about their goals in response to environment changes. Our hypothesis is that GDA enables an agent to respond more effectively to unexpected events in complex environments. We instantiate the GDA model in ARTUE (A utonomous R esponse t o U nexpected E vents), a domain‐independent autonomous agent. We evaluate ARTUE on scenarios from two complex strategy simulations, and report on its comparative benefits and limitations. By employing goal reasoning, ARTUE outperforms an off‐line planner and a discrepancy‐based replanner on scenarios requiring reasoning about unobserved objects and facts and on scenarios presenting opportunities outside the scope of its current mission.