高级认知过程对人脑信号活动模式的影响及认知系统仿真*

为了帮助解决脑机接口中人脑信号与高级认知活动之间的对接问题,建立了同时模拟高级认知过程与人脑信号模式的认知模型。采用的方法是ACT-R认知仿真实验结合fMRI脑影像实验。以简化四方趣题为实验范式,设计有锚和无锚两类不同复杂度任务。从ACT-R认知仿真实验分析出高级认知过程,从fMRI实验结果分析出感兴趣脑区BOLD信号模式。通过对两实验结果进一步分析得出,不同的高级认知活动过程对脑功能区BOLD信号模式存在多处影响,包括启动值和BOLD的峰值位置、平均变化量、上升变化量以及下降变化量。本研究获得了高级认知     

启发式搜索问题研究

探讨了问题空间中的启发式搜索技术,给出了构造启发式函数的基本方法,指出提高启发能力的措施和影响启发式搜索效率的主要因素。     

浅谈人工智能中的启发式搜索策略

人工智能所要解决的问题大部分是非结构化或结构不良的问题,启发式搜索可以极大提高效率。讲述了搜索策略中的启发式搜索,对它的原理进行讲解,前景进行了展望。     

浅析计算机人工智能启发式搜索函数

阐述了人工智能的核心问题及启发式搜索函数的基本概念,介绍了4种经典问题启发式搜索函数的选择及其研究中遇到的难题,并从中求解来探讨解决问题的思路。     

非结构化P2P资源搜索策略研究

通过从盲目搜索策略和启发式搜索策略两方面来分析无结构化P2P(peer-to-peer)系统的资源搜索策略。从泛洪搜索算法、迭代泛洪算法、随机漫步搜索算法以及基于超级节点的查询算法等四个方面对盲目搜索策略进行分析,而从基于兴趣的查询算法、移动代理、高速缓存及启发式泛洪搜索算法等四个方面对启发式搜索算法进行阐述。最后在以上算法分析的基础上对未来的工作进行了展望。     

启发式搜索在数学智能解题系统中的应用研究

在人工智能领域,对问题求解的方法都需要某种对解答的搜索,即为一个搜索过程.文中论述的数学智能辅导系统采用了与或树知识表示方法,也可称为问题规约法.它把初始问题通过一系列变换最终变为一个子问题集合,而这些子问题的解可以直接得到,从而解答了初始问题.系统使用以推理深度作估价函数的启发式搜索,使得问题的求解更加有效与合理.论述了采用启发式搜索的必要性及可行性.对比了采用启发式搜索前后,系统解题合理性得到很大提高.     

一种基于启发式搜索策略的曲线重构算法

本文提出了一种基于启发式搜索策略的曲线重构算法。该算法通过对数据点集做三角化剖分,构造势函数并建立其相应数据点间的加权连通关系,然后进一步利用启发式搜索AStar算法求解对应的优化路径,最后对所得的有序数据点用MLS方法获得重构曲线。实验结果表明,本文方法可较好地保持数据点集的形状和走向,有效降低噪声点对重构曲线的影响,具有很强的适应性和鲁棒性。     

求解仿真优化问题的知识型启发式搜索方法

仿真优化研究基于仿真的目标优化问题,已经成为系统仿真和运筹学等领域共同关注的热点和前沿课题.针对离散事件动态系统仿真优化中的难点问题,提出了一种全新的知识型启发式搜索方法.采用知识模型和启发式搜索模型相结合的集成建模思路,以启发式搜索模型为基础,同时突出知识模型的作用,将启发式搜索模型和知识模型进行优化组合、优势互补,以提高启发式搜索技术的效率.基于期望值模型的数值仿真,验证了方法的可行性和有效性.仿真结果表明,无论是求解质量还是求解速度,都优于其它几种现有方法.研究结果表明,将知识模型合理地嵌入到现有启发式搜索方法中,可以有效地解决复杂的仿真优化问题.     

状态空间的启发式搜索方法研究

对人工智能中用于状态空间问题求解的启发式搜索方法--A算法和A*算法进行了详细分析,并指出了影响搜索算法启发能力的主要因素和提高搜索效率的措施.     

A tabu search heuristic for the truck and trailer routing problem

Two new construction heuristics and a tabu search heuristic are presented for the truck and trailer routing problem, a variant of the vehicle routing problem. Computational results indicate that the heuristics are competitive to the existing approaches. The tabu search algorithm obtained better solutions for each of 21 benchmark problems.     

The pathology of heuristic search in the 8-puzzle

In practice, an incomplete heuristic search nearly always finds better solutions if it is allowed to search deeper, i.e. expand and heuristically evaluate more nodes in the search tree. On the rare occasions when searching deeper is not beneficial, a curious phenomenon called ‘search pathology’ occurs. In this article, we study the pathology and gain of a deeper search of the minimin algorithm in the 8-puzzle, a domain often used for evaluating single-agent search algorithms. We have analysed the influence of various properties of the search tree and the heuristic evaluation function on the gain and the pathology. In order to investigate a broad range of the properties, the original 8-puzzle was extended with diagonal moves, yielding a larger variety of search trees. It turned out that in the 8-puzzle, a substantial proportion of the solvable positions is pathological under various parameters. More importantly, the search parameters that enable the highest gains are quite consistent in pathological and non-pathological positions alike, thus pointing to potentially successful search strategies.     

Bidirectional heuristic search with limited resources

Heuristic search strategies have useful applicable problem solving in AI. It has been observed that bidirectional heuristic search algorithms can be potentially than their unidirectional counterparts. However, the problem with bidirectional search in practice is to make the two search fronts (forward and backward) meet in the middle. De Champeaux suggested a front-to-front algorithm [3] that overcomes this problem. But the disadvantage of that algorithm is that it is computationally very expensive. In this paper, we suggest a new front-to-front algorithm that is computationally much less expensive. Our algorithm does not guarantee optimality always, but its solution quality and execution time can be controlled by some external parameters. Finally, we present some experimental results on a generic state space problem, viz. 15-puzzle, showing the effectiveness of our algorithm.     

博弈树启发式搜索的α-β剪枝技术研究

博弈是启发式搜索的一个重要应用领域,博弈的过程可以用一棵博弈搜索树表示,通过对博弈树进行搜索求取问题的解,搜索策略常采用α-β剪枝技术。在深入研究α-β剪枝技术的基础上,提出在扩展未达到规定深度节点时,对扩展出的子节点按照估价函数大小顺序插入到搜索树中,从而在α-β剪枝过程中剪掉更多的分枝,提高搜索效率。     

MAS系统的问题求解能力分析

本文用状态空间搜索模型分析了多Agent系统(MAS)的问题求解能力，认为MAS系统中Agent之间知识的组合应用和对问题搜索方向的交互和决策是影响MAS系统问题求解能力的主要原因，在状态空间搜索模型下可以将Agent间知识的组合应用表达为不同Agent的搜索路径的组合，而Agent对搜索方向的判断是基于启发式信息做出的，从而为形式化分析MAS系统的性能建立了通用的模型．本文以A*算法为例探讨了可采纳算法下多Agent合作求解效果与Agent的知识和启发信息之间的关系，指出只有在一定条件下MAS系统才会获得更好的解题能力．本文还对非可采纳算法下MAS系统性能分析方法提出了初步看法．     

Multiobjective heuristic search in road maps

This article considers the application of exact multiobjective techniques to search in large size realistic road maps. In particular, the NAMOA^∗ algorithm is successfully applied to several road networks from the DIMACS shortest path implementation challenge with two objectives. An efficient heuristic function previously proposed by Tung and Chew is evaluated. Heuristic values are precalculated with search. The precalculation effort is shown to pay off during the multiobjective search stage. An improvement to the calculation procedure is also proposed, resulting in added improved time performance in many problem instances.     

Controlling the learning process of real-time heuristic search

Real-time search provides an attractive framework for intelligent autonomous agents, as it allows us to model an agent's ability to improve its performance through experience. However, the behavior of real-time search agents is far from rational during the learning (convergence) process, in that they fail to balance the efforts to achieve a short-term goal (i.e., to safely arrive at a goal state in the present problem solving trial) and a long-term goal (to find better solutions through repeated trials). As a remedy, we introduce two techniques for controlling the amount of exploration, both overall and per trial. The weighted real-time search reduces the overall amount of exploration and accelerates convergence. It sacrifices admissibility but provides a nontrivial bound on the converged solution cost. The real-time search with upper bounds insures solution quality in each trial when the state space is undirected. These techniques result in a convergence process more stable compared with that of the Learning Real-Time algorithm.     

Neural networks and heuristic search

This surveys the recent developments of applying neural networks to heuristic search. Special focus is given to three categories of applications: combinatorial optimization, rule-based inference, and modeling assistance. The avenues for research point to additional opportunities and some of the mathematical problems that remain to be solved.