Constructive belief and rational representation

It is commonplace in artificial intelligence to divide an agent's explicit beliefs into two parts: the beliefs explicitly represented or manifest in memory, and the implicitly represented or constructive beliefs that are repeatedly reconstructed when needed rather than memorized. Many theories of knowledge view the relation between manifest and constructive beliefs as a logical relation, with the manifest beliefs representing the constructive beliefs through a logic of belief. This view, however, limits the ability of a theory to treat incomplete or inconsistent sets of beliefs in useful ways. We argue that a more illuminating view is that belief is the result of rational representation. In this theory, the agent obtains its constructive beliefs by using its manifest beliefs and preferences to rationally (in the sense of decision theory) choose the most useful conclusions indicated by the manifest beliefs.     

Computation of algebraic numbers and arithmetic operations over them with linear memory

Known algorithms for computing algebraic numbers and algorithms for arithmetic operations over them as operations over constructive real numbers are considered. It is shown that these algorithms are linear in memory.     

Solving the vehicle routing problem with adaptive memory programming methodology

In this paper we develop an adaptive memory programming method for solving the capacitated vehicle routing problem called Solutions’ Elite PArts Search (SEPAS). This iterative method, first generates initial solutions via a systematic diversification technique and stores their routes in an adaptive memory. Subsequently, a constructive heuristic merges route components (called elite parts) from those in the adaptive memory. Finally, a tabu search approach improves the heuristically constructed solution and the adaptive memory is appropriately updated. SEPAS has been tested on two benchmark data sets and provides high quality solutions in short computational times for all problem instances. The method reaches several new best solutions for benchmark instances with a large number of customers.     

Representation of concept lattices by bidirectional associative memories

This article presents a concept interpretation of patterns for bidirectional associative memory (BAM) and a representation of hierarchical structures of concepts (concept lattices) by BAMs. The constructive representation theorem provides a storing rule for a training set that allows a concept interpretation. Examples demonstrating the theorems are presented.     

分布式系统的移动代理保障检测机制

移动代理技术为解决复杂、动态、分布式的应用提供了新的计算手段,结合Agent技术的特点以及Agent技术与分布式系统的相互适应性,进行一种基于Agent技术的用来对分布计算机网络或服务个体行为关键点进行监测和信息收集的研究,提出一种可行的移动代理保障检测机制,为分布式系统提供主动的分布式系统可靠性保障情况报告和建议性措施。     

关于一般存储器神经网络的学习收敛性研究

本文在一类称为一般存储器神经网络(General Memory Neural Network(GMNN))的统一框架下来研究学习收敛性。该一般模型类的结构由三部分组成：输入空间量化、存储器地址产生器、查表式某种组合输出。当产生的地址是固定有限的个数以及网络输出是线性求和时，可以证明GMNN能在最小平方误差意义下收敛。CMAC(Cerebellar Model Articulation Controller)、SLLUP(Single—Layer Look up Perceptrons)是该类模型的典型代表。本文的意义在于为构造新的基于局部学习的神经网络模型提供理论指导，最后给出了这种构造的两个例子——SDM(Sparse Distributed Memory)和SLLUP的两个推广模型。     

Embodied spatial cognition: Biological and artificial systems

In this paper, we sketch out a computational theory of spatial cognition motivated by navigational behaviours, ecological requirements, and neural mechanisms as identified in animals and man. Spatial cognition is considered in the context of a cognitive agent built around the action–perception cycle. Besides sensors and effectors, the agent comprises multiple memory structures including a working memory and a longterm memory stage. Spatial longterm memory is modelled along the graph approach, treating recognizable places or poses as nodes and navigational actions as links. Models of working memory and its interaction with reference memory are discussed. The model provides an overall framework of spatial cognition which can be adapted to model different levels of behavioural complexity as well as interactions between working and longterm memory. A number of design questions for building cognitive robots are derived from comparison with biological systems and discussed in the paper.     

Tabu search for scheduling on identical parallel machines to minimize mean tardiness

This paper presents a tabu search approach for scheduling jobs on identical parallel machines with the objective of minimizing the mean tardiness. Initially, we consider a basic tabu search that uses short term memory only. Local search is performed on a neighborhood defined by two types of moves. Insert moves consist of transferring each job from one machine to another and swap moves are those obtained by exchanging each pair of jobs between two machines. Next, we analyze the incorporation of two diversification strategies with the aim of exploring unvisited regions of the solution space. The first strategy uses long term memory to store the frequency of the moves executed throughout the search and the second makes use of influential moves. Computational tests are performed on problems with up to 10 machines and 150 jobs. The heuristic performance is evaluated through a lower bound given by Lagrangean relaxation. A comparison is also made with respect to the best constructive heuristic reported in the literature.     

Partial information basis for agent-based collaborative dialogue

We propose a partial information state-based framework for collaborative dialogue and argument between agents. We employ a three-valued based nonmonotonic logic, NML3, for representing and reasoning about Partial Information States (PIS). NML3 formalizes some aspects of revisable reasoning and it is sound and complete. Within the framework of NML3, we present a formalization of some basic dialogue moves and the rules of protocols of some types of dialogue. The rules of a protocol are nonmonotonic in the sense that the set of propositions to which an agent is committed and the validity of moves vary from one move to another. The use of PIS allows an agent to expand consistently its viewpoint with some of the propositions to which another agent, involved in a dialogue, is overtly committed. A proof method for the logic NML3 has been successfully implemented as an automatic theorem prover. We show, via some examples, that the tableau method employed to implement the theorem prover allows an agent, absolute access to every stage of a proof process. This access is useful for constructive argumentation and for finding cooperative and/or informative answers.     

Mobile Agent-Based Memory Tracer Using Host-Based Intrusion Detection System for Safeguarding Critical Memory

ABSTRACT

Intrusion in a host may cause damage to the critical memory, which contains important official files or databases. The mobile agent, a piece of code that makes parallel run on host, migrates through the network and performs the requested action. In this paper, we declare a new intrusion detection model using Host-Based Intrusion Detection System (HIDS) for identification and recovery strategies. A memory trace model has been designed to identify the intrusion detection by using a memory tracer mobile agent. Memory Checksum plays a vital role in the identification of the intrusion. This model is designed for intrusion into a host, which alters the critical memory.     

基于FAM的模糊神经控制器的研究

根据模糊联想记忆(FAM)理论, 提出了预解模糊FAM原理, 给出了预解模糊FAM和一般FAM的等价性的构造性证明. 为了提高FAM推理过程的自适应能力, 将神经网络应用于预解模糊FAM推理, 提出了一种新的智能控制器——FAM神经控制器(FAMNC), 以小车倒立摆为控制对象进行了仿真研究, 表明了所提方法的可行性.     

蚁群智能体记忆模型研究

将记忆机制引入传统蚁群算法,把蚂蚁看作具有记忆的智能体,通过对记忆的存储、更新及遗忘原理进行分析,建立一种基于生物记忆原理的蚁群智能体记忆模型。在模型中,蚂蚁智能体能够凭借记忆对自身记忆库和蚁群记忆库进行不断搜索和更新,及时调整当前最优路径。记忆原理与传统蚁群算法的结合替代了后者多次反复迭代的寻优模式,能更好地实现路径选优、缩短搜索时间、提高算法执行效率。实践检验发现,该模型能实时追踪蚁群智能体的最新动态,对当前最优路径做出及时调整和判断,从而引导自身及其他蚂蚁智能体准确、高效地寻找到最优路径。     

Adaptive memory programing for the robust capacitated international sourcing problem

The International Sourcing Problem consists of selecting a subset from an available set of potential suppliers internationally located. The selected suppliers must meet the demand for items from a set of plants, which are also located worldwide. Since the costs are affected by macroeconomic conditions in the countries where the supplier and the plant are located, the formulation considers the uncertainty associated with changes in these conditions. We formulate the robust capacitated international sourcing problem by means of a scenario-optimization approach. When dealing with uncertainty, one of the most common approaches in the literature is to formulate the problem via a set of possible scenarios, this is the approach we follow here. In this paper we propose a constructive method based on memory structures to solve this problem. The method is coupled with a local search procedure followed by a path relinking for improved outcomes. We propose an innovative mechanism to achieve a good balance between intensification and diversification in the search process. Moreover, our path-relinking implementation uses constructive neighborhoods for extrapolated relinking. The computational experimentation favors this method when compared with a recent tabu search approach.     

分布式智能故障诊断模型的MAS实现研究

该文分析了基于MAS的分布式智能故障诊断方法和过程,提出并实现了一种任务分解的Agent诊断系统结构及原型系统。在某企业安全监控系统的应用中,该模型能快速、准确地进行故障成因分析,并给出合理的、建设性的决策意见。     

A dynamic resource management in mobile agent by artificial neural network

In this paper, a resource management for dynamic load balancing in mobile agent by artificial neural network scheme (ANN-DLB) is presented to maximize the number of the served tasks in developing high performance cluster. This dynamic load balance with the growth of the service type and user number in the mobile networks of the higher performance is required in service provision and throughput. Most of the conventional policies are used in load indices with the threshold value to decide the load status of the agent hosts by CPU or memory. The main factor influencing the workload is the competitions among the computing resources such as CPU, memory, I/O and network. There are certain I/O data of the intensive applications where load balancing becomes the important issue. This relationship between the computing resources is very complex to define the rules for deciding the workload. This paper proposed a new dynamic load balancing for evaluating the agent hosts’ workload with the artificial neural network (ANN). By applying the automatic learning of the back-propagation network (BPN) model can establish the ANN model and also can measure the agent host loading with five inputs: CPU, memory, I/O, network and run-queue length. The structure of the load balancing system is composed of three design agents: the load index agent (LIA), the resource management agent (RMA) and the load transfer agent (LTA). These experimental results reveal that the proposed ANN-DLB yields better performance than the other methods. These results demonstrate that the proposed method has high throughput, short response time and turnaround time, and less agent host negotiation complexity and migrating tasks than the previous methods.     

Simultaneous Pattern Classification and Multidomain Association Using Self-Structuring Kernel Memory Networks

In this paper, a novel exemplar-based constructive approach using kernels is proposed for simultaneous pattern classification and multidomain pattern association tasks. The kernel networks are constructed on a modular basis by a simple one-shot self-structuring algorithm motivated from the traditional Hebbian principle and then, they act as the flexible memory capable of generalization for the respective classes. In the self-structuring kernel memory (SSKM), any arduous and iterative network parameter tuning is not involved for establishing the weight connections during the construction, unlike conventional approaches, and thereby, it is considered that the networks do not inherently suffer from the associated numerical instability. Then, the approach is extended for multidomain pattern association, in which a particular domain input cannot only activate some kernel units (KUs) but also the kernels in other domain(s) via the cross-domain connection(s) in between. Thereby, the SSKM can be regarded as a simultaneous pattern classifier and associator. In the simulation study for pattern classification, it is justified that an SSKM consisting of distinct kernel networks can yield relatively compact-sized pattern classifiers, while preserving a reasonably high generalization capability, in comparison with the approach using support vector machines (SVMs)     

Temporal coding: assembly formation through constructive interference

Temporal coding is studied for an oscillatory neural network model with synchronization and acceleration. The latter mechanism refers to increasing (decreasing) the phase velocity of each unit for stronger (weaker) or more coherent (decoherent) input from the other units. It has been demonstrated that acceleration generates the desynchronization that is needed for self-organized segmentation of two overlapping patterns. In this letter, we continue the discussion of this remarkable feature, giving also an example with several overlapping patterns. Due to acceleration, Hebbian memory implies a frequency spectrum for pure pattern states, defined as coherent patterns with decoherent overlapping patterns. With reference to this frequency spectrum and related frequency bands, the process of pattern retrieval, corresponding to the formation of temporal coding assemblies, is described as resulting from constructive interference (with frequency differences due to acceleration) and phase locking (due to synchronization).     

Smart task support through proactive access to organizational memory

We describe an approach towards integrating the semantics of semi-structured documents with task-support for (weakly structured) business processes and proactive inferencing capabilities of a desk support agent. The mechanism of our Proactive Inferencing Agent is motivated by the requirements posed in (weakly structured) business processes performed by a typical knowledge worker and by experiences we have made from a first trial with a Reactive Agent Support scheme.Our reactive scheme is an innovative approach for smart task support that links knowledge from an organizational memory to business tasks. The scheme is extended to include proactive inferencing capabilities in order to improve user-friendliness and to facilitate modeling of actual agent support. In particular, the improved scheme copes with varying precision of knowledge found in the organizational memory and it reasons proactively about what might be interesting to you and what might be due in your next step.     

Guest Editors' Introduction: Advanced Heuristics in Transportation and Logistics

Transportation and logistics organizations often face large-scale combinatorial problems on both operational and strategic levels. By exploiting problem-specific characteristics, classical heuristic methods--such as constructive and iterative local search methods--aim at a relatively limited exploration of the search space, thereby producing acceptable-quality solutions in modest computing times. In a major departure from a classical heuristic, a metaheuristic method exploits not only the problem characteristics but also ideas based on artificial intelligence methodologies, such as different types of memory structures and learning mechanisms, as well as analogies with optimization methods found in nature. Solutions produced by metaheuristics typically are of a much higher quality than those obtained with classical heuristic approaches.This article is part of a special issue on advanced heuristics in transportation and logistics.