Simulation of carbon-based model for virtual plants as complex adaptive system

This research presented a teleonomic-based simulation approach to virtual plants integrating the technology of intelligent agent as well as the knowledge of plant physiology and morphology. Plant is represented as the individual metamers and root agents with both functional and geometrical structure. The development of plant is achieved by the flush growth of metamer and root agents controlled by their internal physiological status and external environment. The eggplant based simulation results show that simple rules and actions (internal carbon allocation among organs, dynamic carbon reserve/mobilization, carbon transport in parallel using a discrete pressure-flow paradigm and child agent position choosing for maximum light interception, etc.) executed by agents can cause the complex adaptive behaviors on the whole plant level: carbon partitioning among metamers and roots, carbon reserve dynamics, architecture and biomass adaptation to environmental heterogeneity and the phototropism, etc. This phenomenon manifest that the virtual plant simulated in presented approach can be viewed as a complex adaptive system.     

Multi-Agent Information Fusion System to manage data from a WSN in a residential home

With the increase of intelligent systems based on Multi-Agent Systems (MAS) and the use of Wireless Sensor Networks (WSN) in context-aware scenarios, information fusion has become an essential part of this kind of systems where the information is distributed among nodes or agents. This paper presents a new MAS specially designed to manage data from WSNs, which was tested in a residential home for the elderly. The proposed MAS architecture is based on virtual organizations, and incorporates social behaviors to improve the information fusion processes. The data that the system manages and analyzes correspond to the actual data of the activities of a resident. Data is collected as the information event counts detected by the sensors in a specific time interval, typically one day. We have designed a system that improves the quality of life of dependant people, especially elderly, by fusioning data obtained by multiple sensors and information of their daily activities. The high development of systems that extract and store information make essential to improve the mechanisms to deal with the avalanche of context data. In our case, the MAS approach results appropriated because each agent can represent an autonomous entity with different capabilities and offering different services but collaborating among them. Several tests have been performed to evaluate this platform and preliminary results and the conclusions are presented in this paper.     

催化燃烧式瓦斯传感器技术研究进展

首先分析了催化燃烧式瓦斯传感器的工作原理,然后综述了催化燃烧式瓦斯传感器的标定与补偿技术现状,最后指出,综合考虑催化燃烧式瓦斯传感器的稳定性和一致性,探索研究在初始不一致和工作过程不稳定的双重作用下,传感器数据的变化规律,通过智能信息处理的方法予以动态补偿,将是下一步需要研究的主要方向。     

Increasing pattern recognition accuracy for chemical sensing by evolutionary based drift compensation

Artificial olfaction systems, which mimic human olfaction by using arrays of gas chemical sensors combined with pattern recognition methods, represent a potentially low-cost tool in many areas of industry such as perfumery, food and drink production, clinical diagnosis, health and safety, environmental monitoring and process control. However, successful applications of these systems are still largely limited to specialized laboratories. Sensor drift, i.e., the lack of a sensor’s stability over time, still limits real industrial setups. This paper presents and discusses an evolutionary based adaptive drift-correction method designed to work with state-of-the-art classification systems. The proposed approach exploits a cutting-edge evolutionary strategy to iteratively tweak the coefficients of a linear transformation which can transparently correct raw sensors’ measures thus mitigating the negative effects of the drift. The method learns the optimal correction strategy without the use of models or other hypotheses on the behavior of the physical chemical sensors.     

Mental states as multi-context systems

Although Belief-Desire-Intention (BDI) agents have been deeply investigated from both a theoretical and a pragmatic perspective, less attention has been paid to the inherent recursive structure of mental states, which plays an essential role when modelling high level interaction between intelligent agents. This paper tries to capture this property by introducing a multi-context approach to the representation of mental states. A semantics for multi-context formalisms is provided based on the definition of “mental structure”, which is a hierarchical lattice of triangular modules <x,B,D>, where the component x represents the agent x’s mental state as a whole, while B and D represent specifically x’s beliefs and x’s desires. If other mental attitudes, as intention and commitment, are to be considers as primitives, then they can be embodied in the basic module, otherwise they can be represented in terms of beliefs and desires. The old notion of clause is rediscovered in order to facilitate the heavy automated theorem-proving necessary to exploit the potentiality of the formalism for the intelligent interaction with the external environment. The main advantages of this approach are the support for “unconsciousness” and the fact that inferences themselves can be modelled as mental attitudes. Some advanced dynamics of mental states, as the abductive revision of mental states after the reception of a communication, will easily be applied over this formalism.      

ACHIEVING FLEXIBLE AUTONOMY IN MULTIAGENT SYSTEMS USING CONSTRAINTS

Organizations influence many aspects of our lives. They exist for one reason: they can accomplish things that individuals cannot. While recent work in high-autonomy systems has shown that autonomy is a critical issue in artificial intelligence (AI) systems, these systems must also be able to cooperate with and rely on one another to deal with complex problems. The autonomy of such systems must be flexible, in order that agents may solve problems on their own as well as in groups. We have developed a model of distributed problem solving in which coordination of problem-solving agents is viewed as a multiagent constraint-satisfaction planning problem. This paper describes the experimental testbed that we are currently developing to facilitate the investigation of various constraint-based strategies for addressing the coordination issues inherent in cooperative distributed problem-solving domains.     

Multiple Adaptive Agents for Tactical Driving

Recent research in automated highway systems has ranged from low-level vision-based controllers to high-level route-guidance software. However, there is currently no system for tactical-level reasoning. Such a system should address tasks such as passing cars, making exits on time, and merging into a traffic stream. Many previous approaches have attempted to hand construct large rule-based systems which capture the interactions between multiple input sensors, dynamic and potentially conflicting subgoals, and changing roadway conditions. However, these systems are extremely difficult to design due to the large number of rules, the manual tuning of parameters within the rules, and the complex interactions between the rules. Our approach to this intermediate-level planning is a system which consists of a collection of autonomous agents, each of which specializes in a particular aspect of tactical driving. Each agent examines a subset of the intelligent vehicle's sensors and independently recommends driving decisions based on their local assessment of the tactical situation. This distributed framework allows different reasoning agents to be implemented using different algorithms.When using a collection of agents to solve a single task, it is vital to carefully consider the interactions between the agents. Since each reasoning object contains several internal parameters, manually finding values for these parameters while accounting for the agents' possible interactions is a tedious and error-prone task. In our system, these parameters, and the system's overall dependence on each agent, is automatically tuned using a novel evolutionary optimization strategy, termed Population-Based Incremental Learning (PBIL).Our system, which employs multiple automatically trained agents, can competently drive a vehicle, both in terms of the user-defined evaluation metric, and as measured by their behavior on several driving situations culled from real-life experience. In this article, we describe a method for multiple agent integration which is applied to the automated highway system domain. However, it also generalizes to many complex robotics tasks where multiple interacting modules must simultaneously be configured without individual module feedback.     

社群演化的稳健迁移估计及演化离群点检测

时序数据集中的社群演化模式是网络行为动力学研究与应用的重要领域.基于社群演化的离群点检测不仅能够发现新颖的异常行为模式,同时也有利于更准确地理解社群的演化趋势.运用成员关于社群隶属关系的变化,提出了社群演化迁移矩阵的概念,研究并揭示了迁移矩阵的若干性质及其与社群结构演化之间的关系.在采用稳健回归M-估计方法进一步优化迁移矩阵降低异常点干扰的同时,对社群演化离群点加以刻画和定义.鉴于复杂网络包含大量随机游走的边缘个体,所定义的离群点综合考虑其在社群中角色的变化和相对于社群总体迁移模式的差异.基于上述思想提出的演化离群点检测算法能够适应各类社群演化趋势,更有效地聚焦和发现大规模社会网络中重要成员的异常演化行为.实验结果表明,所提出的方法能够从大规模社会网络演化序列中发现重要的离群演化模式,并在现实中找到合理的解释.     

基于情景演算的多个智能虚拟人协作行为描述语言

在情景演算的框架内引入真并发动作和相应的语义,参照 FIPA-ACL 增加了请求、承诺、结果等通信动作,将 ConGolog 扩展为 CTConGolog,并且基于 CTConGolog 提出一个请求/服务协作模型及相应语义.在此基础上,引入用于表示复杂行为动画的元动作,提出并且实现了一种多个智能虚拟人协作行为描述语言--CBDL.实验结果表明,CBDL 能够较好地描述多个智能虚拟人在动态环境下通过推理和协作而表现出的行为.     

一种基于agent随机扩散的图像分割方法

图像分割是图像处理的关键环节,直接影响以后的分析、识别和解译。根据进化agent具有自适应性、非线性映射和高度并行处理能力等优点,提出了一种基于agent随机扩散的图像分割方法。在该方法中,agent点随机地撒在网格单元上,并在满足一致性标准的区域用标签标定。agent点有复制和扩散两种行为扩散模式,当一个agent成功的找到一个像素满足一致性标准,它将在周围区域复制一系列后代,因此这些后代更容易找到那些满足一致性条件的像素,而对于那些超过生命周期的agent点将停止搜索,从环境中消失。利用医学胸部的CT图像和脑部的磁共振图像进行的实验结果表明,该方法能较好地从图像中提出感兴趣的区域。     

The memorization of in-line sensorimotor invariants: toward behavioral ontogeny and enactive agents

This paper presents a behavioral ontogeny for artificial agents based on the interactive memorization of sensorimotor invariants. The agents are controlled by continuous timed recurrent neural networks (CTRNNs) which bind their sensors and motors within a dynamic system. The behavioral ontogenesis is based on a phylogenetic approach: memorization occurs during the agent’s lifetime and an evolutionary algorithm discovers CTRNN parameters. This shows that sensorimotor invariants can be durably modified through interaction with a guiding agent. After this phase has finished, agents are able to adopt new sensorimotor invariants relative to the environment with no further guidance. We obtained these kinds of behaviors for CTRNNs with 3–6 units, and this paper examines the functioning of those CTRNNs. For instance, they are able to internally simulate guidance when it is externally absent, in line with theories of simulation in neuroscience and the enactive field of cognitive science.     

Energy-efficient adaptive sensor scheduling for target tracking in wireless sensor networks

Sensor scheduling is essential to collaborative target tracking in wireless sensor networks (WSNs). In the existing works for target tracking in WSNs, such as the information-driven sensor query (IDSQ), the tasking sensors are scheduled to maximize the information gain while minimizing the resource cost based on the uniform sampling intervals, ignoring the changing of the target dynamics and the specific desirable tracking goals. This paper proposes a novel energyefficient adaptive sensor scheduling approach that jointly selects tasking sensors and determines their associated sampling intervals according to the predicted tracking accuracy and tracking energy cost. At each time step, the sensors are scheduled in alternative tracking mode, namely, the fast tracking mode with smallest sampling interval or the tracking maintenance mode with larger sampling interval, according to a specified tracking error threshold. The approach employs an extended Kalman filter (EKF)-based estimation technique to predict the tracking accuracy and adopts an energy consumption model to predict the energy cost. Simulation results demonstrate that, compared to a non-adaptive sensor scheduling approach, the proposed approach can save energy cost significantly without degrading the tracking accuracy.     

EVA: AN EVOLUTIONARY APPROACH TO MUTUAL MONITORING OF LEARNING INFORMATION AGENTS

A learning agent system is composed of agents able to autonomously enrich their knowledge and improve their performance, using learning strategies. The idea underlying this article is that individual improvements obtained by the learning capabilities of an agent should be exploited to advantage the other agents, and a natural way of obtaining such a result is represented by evolutionary processes. However, the biological evolutionary mechanisms are often too complex to be reproduced in a software environment. In this context, we argue that cloning, due to its very simple mechanism of reproduction, can be usefully used. In our approach, a user in a virtual community can substitute an unsatisfactory agent cloning an existing agent having both similar interests and a good reputation in the community. This mechanism induces an evolutionary process in the community, such that the less satisfactory agents are replaced by more effective agents. The key issue of this proposal is that of suitably selecting the agent to be cloned in the presence of a user's request, and to this purpose we propose an evolutionary model of reputation. Our evolutionary approach has been implemented on top of a leaning agent-based recommender system, and a number of experiments show that this novel strategy introduces significant improvements in the effectiveness of the recommendations.     

Middleware for Internet of Things: Survey and Challenges

The Internet of things (IoT) applications span many potential fields. Furthermore, smart homes, smart cities, smart vehicular networks, and healthcare are very attractive and intelligent applications. In most of these applications, the system consists of smart objects that are equipped by sensors and Radio Frequency Identification (RFID) and may rely on other technological computing and paradigm solutions such as M2M (machine to machine) computing, Wifi, Wimax, LTE, cloud computing, etc. Thus, the IoT vision foresees that we can shift from traditional sensor networks to pervasive systems, which deliver intelligent automation by running services on objects. Actually, a significant attention has been given to designing a middleware that supports many features; heterogeneity, mobility, scalability, multiplicity, and security. This papers reviews the-state-of-the-art techniques for IoT middleware systems and reveals an interesting classification for these systems into service and agent-oriented systems. Therefore two visions have emerged to provide the IoT middleware systems: Via designing the middleware for IoT system as an eco-system of services or as an eco-system of agents. The most common feature of the two approaches is the ability to overcome heterogeneity issues. However, the agent approach provides context awareness and intelligent elements. The review presented in this paper includes a detailed comparison between the IoT middleware approaches. The paper also explores challenges that form directions for future research on IoT middleware systems. Some of the challenges arise, because some crucial features are not provided (or at most partially provided) by the existing middleware systems, while others have not been yet tackled by current research in IoT.     

A multi-agent fuzzy consensus model in a Situation Awareness framework

In order to define systems enabling the automatic identification of occurring situations, numerous approaches employing intelligent software agents to analyse data coming from deployed sensors have been proposed. Thus, it is possible that more agents are committed to monitor the same phenomenon in the same environment. Redundancy of sensors and agents is needed, for instance, in real world applications in order to mitigate the risk of faults and threats. One of the possible side effects produced by redundancy is that agents, observing the same phenomenon, could provide discordant opinions. Indeed, solid mechanisms for reaching an agreement among these agents and produce a shared consensus on the same observations are needed. This paper proposes an approach to integrate a fuzzy-based consensus model into a Situation Awareness framework. The main idea is to consider intelligent agents as experts claiming their opinions (preferences) on a phenomenon of interest.     

Semantical considerations on intention dynamics for BDI agents

The BDI paradigm is a powerful means for constructing intelligent agents in terms of their beliefs, desires, and intentions. For this paradigm to bear its full potential, it must incorporate considerations from rationality. This paper develops a set of postulates for intelligent agents who deliberate about their intentions and actions. However, even simple postulates can lead to paradoxical results when formalized naively. We propose an approach based on temporal possibility and action that avoids those problems. This approach incorporates a formal model based on branching time in which a probabilistic analysis of choice can be captured. In this manner, the intuitions of the BDI paradigm can be reconciled with those of rational agency.     

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.     

Evolutionary testing of autonomous software agents

A system built in terms of autonomous software agents may require even greater correctness assurance than one that is merely reacting to the immediate control of its users. Agents make substantial decisions for themselves, so thorough testing is an important consideration. However, autonomy also makes testing harder; by their nature, autonomous agents may react in different ways to the same inputs over time, because, for instance they have changeable goals and knowledge. For this reason, we argue that testing of autonomous agents requires a procedure that caters for a wide range of test case contexts, and that can search for the most demanding of these test cases, even when they are not apparent to the agents’ developers. In this paper, we address this problem, introducing and evaluating an approach to testing autonomous agents that uses evolutionary optimisation to generate demanding test cases. We propose a methodology to derive objective (fitness) functions that drive evolutionary algorithms, and evaluate the overall approach with two simulated autonomous agents. The obtained results show that our approach is effective in finding good test cases automatically.