A familiarity-based trust model for effective selection of sellers in multiagent e-commerce systems

In electronic marketplaces, trust is modeled, for instance, in order to allow buying agents to make effective selection of selling agents. Familiarity is often considered to be an important factor in determining the level of trust. In previous research, familiarity between two agents has been simply assumed to be the similarity between them. We propose an improved familiarity measurement based on the exploration of factors that affect a human’s feelings of familiarity. We also carry out experiments to show that the trust model with our improved familiarity measurement is more effective and more stable.     

TRAVOS: Trust and Reputation in the Context of Inaccurate Information Sources

In many dynamic open systems, agents have to interact with one another to achieve their goals. Here, agents may be self-interested, and when trusted to perform an action for another, may betray that trust by not performing the action as required. In addition, due to the size of such systems, agents will often interact with other agents with which they have little or no past experience. There is therefore a need to develop a model of trust and reputation that will ensure good interactions among software agents in large scale open systems. Against this background, we have developed TRAVOS (Trust and Reputation model for Agent-based Virtual OrganisationS) which models an agent’s trust in an interaction partner. Specifically, trust is calculated using probability theory taking account of past interactions between agents, and when there is a lack of personal experience between agents, the model draws upon reputation information gathered from third parties. In this latter case, we pay particular attention to handling the possibility that reputation information may be inaccurate.     

Using argumentation to model agent decision making in economic experiments

In this paper we demonstrate how a qualitative framework for decision making can be used to model scenarios from experimental economic studies and we show how our approach explains the results that have been reported from such studies. Our framework is an argumentation-based one in which the social values promoted or demoted by alternative action options are explicitly represented. Our particular representation is used to model the Dictator Game and the Ultimatum Game, which are simple interactions in which it must be decided how a sum of money will be divided between the players in the games. Studies have been conducted into how humans act in such games and the results are not explained by a decision-model that assumes that the participants are purely self-interested utility-maximisers. Some studies further suggest that differences in choices made in different cultures may reflect their day to day behaviour, which can in turn be related to the values of the subjects, and how they order their values. In this paper we show how these interactions can be modelled in agent systems in a framework that makes explicit the reasons for the agents’ choices based upon their social values. Our framework is intended for use in situations where agents are required to be adaptable, for example, where agents may prefer different outcome states in transactions involving different types of counter-parties.     

Agent long-term coalition credit

Agent trust researches become more and more important because they will ensure good interactions among the software agents in large-scale open systems. Moreover, individual agents often interact with long-term coalitions such as some E-commerce web sites. So the agents should choose a coalition based on utility and trust. Unfortunately, few studies have been done on agent coalition credit and there is a need to do it in detail. To this end, a long-term coalition credit model (LCCM) is presented. Furthermore, the relationship between coalition credit and coalition payoff is also attended. LCCM consists of internal trust based on agent direct interactions and external reputation based on agent direct observation. Generalization of LCCM can be demonstrated through experiments applied in both cooperative and competitive domain environment. Experimental results show that LCCM is capable of coalition credit computation efficiently and can properly reflect various factors effect on coalition credit. Another important advantage that is a useful and basic property of credit is that LCCM can effectively filter inaccurate or lying information among interactions.     

Multi‐agent collaboration based on enhanced cognitive awareness: an architecture for agents' profiling on the semantic web

This paper applies cognitive models, inspired by cognitive science, with the aim to propose architectural and knowledge‐based requirements to structure ontological models for the cognitive profiling of agents. The proposed architecture aims to address the lack of flexibility that most agent‐based collaborations are affected by. The resulting agents, equipped with advanced cognitive profiling, have an increased cognitive awareness of themselves and are more capable of interacting with other agents in a multi‐agents based environment. In this research, cognitive awareness identifies the ability of the web agents to diagnose their processing limitations and to establish interactions with the external environment. The outcome is the enhanced flexibility, reusability and predictability of the agent behaviour; thus contributing towards minimizing human cognitive demands. The concept of cognitive profiling presented in this paper considers the semantic web as an action mediating space, where ontological models provide affordances for improving cognitive awareness through shared knowledge‐base. The conceptual model for the cognitive profile architecture is developed with Protégé Ontology editor to generate OWL Ontology and evaluated through a proof of concept. The results show that agents equipped with cognitive awareness can undertake complex tasks more dynamically.     

Coping with shame and sense of guilt: a Dynamic Logic Account

Aim of this work is to provide a formal characterization of those emotions that deal with normative reasoning, such as shame and sense of guilt, to understand their relation with rational action and to ground their formalization on a cognitive science perspective. In order to do this we need to identify the factors that constitute the preconditions and trigger the reactions of shame and sense of guilt in cognitive agents, that is when agents feel ashamed or guilty and what agents do when they feel so. We will also investigate how agents can induce and silence these feelings in themselves, i.e. the analysis of defensive strategies they can employ. We will argue that agents do have control over their emotions and we will analyze some operations they can carry out on them.     

Using the affordance concept for model design in agent-based simulation

When designing an Agent-Based Simulation Model a central challenge is to formulate the appropriate interactions between agents as well as between agents and their environment. In this contribution we present the idea of capturing agent-environment interactions based on the “affordance” concept. Originating in ecological psychology, affordances represent relations between environmental objects and potential actions that agents may perform using those objects. We assume that explicitly handling affordances based on semantic annotation of entities in simulated space may offer a higher abstraction level for dealing with potential interaction. Our approach has two elements: firstly a methodology for using the affordance concept to identify interactions and secondly a suggestion for integrating affordances into agents’ decision making. We illustrate our approach indicating an agent-based model of after-earthquake behavior.     

Emotions in autonomous agents: comparative analysis of mechanisms and functions

Emotion mechanisms are often used in artificial agents as a method of improving action selection. Comparisons between agents are difficult due to a lack of unity between the theories of emotion, tasks of agents and types of action selection utilised. A set of architectural qualities is proposed as a basis for making comparisons between agents. An analysis of existing agent architectures that include an emotion mechanism can help to triangulate design possibilities within the space outlined by these qualities. With this in mind, twelve autonomous agents incorporating an emotion mechanism into action selection are selected for analysis. Each agent is dissected using these architectural qualities (the agent architecture, the action selection mechanism, the emotion mechanism and emotion state representation, along with the emotion model it is based on). This helps to place the agents within an architectural space, highlights contrasting methods of implementing similar theoretical components, and suggests which architectural aspects are important to performance of tasks. An initial framework is introduced, consisting of a series of recommendations for designing emotion mechanisms within artificial agents, based on correlations between emotion roles performed and the aspects of emotion mechanisms used to perform those roles. The conclusion discusses how problems with this type of research can be resolved and to what extent development of a framework can aid future research.     

Motion sickness in public road transport: the effect of driver, route and vehicle

Relationships between vehicle motion and passenger sickness have been investigated in a survey of 3256 passengers travelling on 56 mainland UK bus or coach journeys. Vehicle motion was measured throughout all journeys, yielding over 110 h of six-axis coach motion data from five types of coach and 17 different drivers. Overall, 28.4% of passengers reported feelings of illness, 12.8% reported nausea and 1.7% reported vomiting during coach travel. Passenger nausea and illness ratings increased with increased exposure to lateral coach motion at low frequencies (< 0.5 Hz). Motion in other axes correlated less well with sickness, although there were some intercorrelations between the motions in the different axes. Sickness levels among passengers were greater with drivers who drove to produce higher average magnitudes of fore-and-aft and lateral vehicle motion. Nausea occurrence was greater on routes classified as being predominantly cross-country where magnitudes of lateral vehicle motion were significantly higher. Lateral motion and motion sickness increased from the front to the rear of each vehicle. No significant differences in sickness were found between the five different vehicle types used in the study. The applicability of a motion sickness dose model to these data is discussed.     

基于多agent的军事物流系统仿真建模方法*

针对军事物流系统复杂性和不确定性的特点,提出了一种基于多agent的系统仿真建模方法。采用中间到两端的多层agents组织方式,引入agent分区概念对agent行为分别进行详细描述。在基于中间层agents逻辑模型的基础上抽象上层agents和下层agents。通过中间层各agent的活动映射而构造的下层agents负责特定的事务处理。中间层agents负责协调下层agents的信息传递,并与上层agents进行交互;上层agents负责协调中间层agents之间的交互。给出了系统模型的仿真实验方法,最后建立了基于多agent的军事物流仿真模型,并通过系统模型的仿真运行,对系统模型的正确性进行了检验。     

Hierarchical multi-channel hidden semi Markov graphical models for activity recognition

Recognizing human actions from a stream of unsegmented sensory observations is important for a number of applications such as surveillance and human-computer interaction. A wide range of graphical models have been proposed for these tasks, and are typically extensions of the generative hidden Markov models (HMMs) or their discriminative counterpart, conditional random fields (CRFs). These extensions typically address one of three key limitations in the basic HMM/CRF formalism – unrealistic models for the duration of a sub-event, not encoding interactions among multiple agents directly and not modeling the inherent hierarchical organization of activities. In our work, we present a family of graphical models that generalize such extensions and simultaneously model event duration, multi agent interactions and hierarchical structure. We also present general algorithms for efficient learning and inference in such models based on local variational approximations. We demonstrate the effectiveness of our framework by developing graphical models for applications in automatic sign language (ASL) recognition, and for gesture and action recognition in videos. Our methods show results comparable to state-of-the-art in the datasets we consider, while requiring far fewer training examples compared to low-level feature based methods.     

DEVISING A TRUST MODEL FOR MULTI-AGENT INTERACTIONS USING CONFIDENCE AND REPUTATION

In open environments in which autonomous agents can break contracts, computational models of trust have an important role to play in determining who to interact with and how interactions unfold. To this end, we develop such a trust model, based on confidence and reputation, and show how it can be concretely applied, using fuzzy sets, to guide agents in evaluating past interactions and in establishing new contracts with one another.     

Utilizing theory of mind for action selection applied in the domain of fighter pilot training

When developing intelligent agents, approaches that allow the anticipation of other agents is of utmost importance. For humans, this has also been shown to be crucial to establish good interactions. In this paper, a design for an agent that is equipped with theory of mind based reasoning capabilities is presented. The approach moves beyond the state of the art from several angles: first, it allows for the expression of certainties with respect to the predicted states of the other agents. Second, it allows the prediction during a substantial number of time steps in the future, thereby utilizing the theory of mind model multiple times. The approach has been applied to the domain of fighter pilots whereby intelligent opponents are developed to facilitate dedicated training for F16 fighter pilots.     

Explicit and implicit animation with fuzzy constraints of a versatile multi‐body system for virtual hand surgery

This work is designed to control the movement of hand structural agents under external action, using the implicit animation driven by explicit animation technique (AI‐CAE technique). Starting from the configuration of a hand at rest obtained by a 3D scanner and after meshing of the structural agents, we seek the configuration of the rigid agents under orthopaedic surgeon external action and interacting reliance of deformable and rigid agents. We have developed a model and software tools to answer this interactive application with adaptive execution. The first contribution comes from notations and definition of a versatile multi‐body system dedicated to the explicit and implicit animation. The second contribution comes from the implicit animation driven by explicit animation itself, and from its ability to mimic the role of cartilages and ligaments. The resulting technique is applied to the bone structure consistency of a specific human hand in the context of virtual hand orthopaedic surgery. The versatile specific multi‐body is made up of hierarchical interacting agents conceivable as a construction set of rigid bones with cartilages–ligaments and underlying links. The explicit animation produces a desired configuration from geometric command parameters of torsion, flexion, pivot and axis shifting, given in a scenario subdivided into temporal sequences. The implicit animation controls the movement by implementing a physics‐based model and fuzzy constraints of position and orientation. It gives better configuration than the explicit animation because it takes into account the interactions between agents, and it gives a neat solution without the problems of complexity due to geometric modelling. A methodology based on the AI‐CAE technique is discussed, medical expertise and validation tests are presented. Copyright © 2011 John Wiley & Sons, Ltd.     

基于免疫网络的策略选择模型研究

研究免疫网络在一类路径选择和规划问题（羊群问题）中的应用。利用免疫网络动力学模型实现羊群问题中主动主体和被动主体之间相互作用,抗体和抗原分别对应选择的策略和动态变化的环境,建立基于免疫网络的主动主体行动策略选择模型。仿真结果表明,该方法中主动主体通过与被动主体之间相互作用,可以比传统方法自发形成概率的、较好的主动主体的行动策略,达到使羊归圈的目的。     

Learning a Generative Model for Multi‐Step Human‐Object Interactions from Videos

Creating dynamic virtual environments consisting of humans interacting with objects is a fundamental problem in computer graphics. While it is well‐accepted that agent interactions play an essential role in synthesizing such scenes, most extant techniques exclusively focus on static scenes, leaving the dynamic component out. In this paper, we present a generative model to synthesize plausible multi‐step dynamic human‐object interactions. Generating multi‐step interactions is challenging since the space of such interactions is exponential in the number of objects, activities, and time steps. We propose to handle this combinatorial complexity by learning a lower dimensional space of plausible human‐object interactions. We use action plots to represent interactions as a sequence of discrete actions along with the participating objects and their states. To build action plots, we present an automatic method that uses state‐of‐the‐art computer vision techniques on RGB videos in order to detect individual objects and their states, extract the involved hands, and recognize the actions performed. The action plots are built from observing videos of everyday activities and are used to train a generative model based on a Recurrent Neural Network (RNN). The network learns the causal dependencies and constraints between individual actions and can be used to generate novel and diverse multi‐step human‐object interactions. Our representation and generative model allows new capabilities in a variety of applications such as interaction prediction, animation synthesis, and motion planning for a real robotic agent.     

A survey of imperatives and action representation formalisms

Representation and reasoning of actions is a wide spread area in the domain of Artificial Intelligence. The representation involves natural language instructions, which are based on the linguistic concepts and the reasoning methodology deals with the logical structures. In the computational domain, several theories pertaining to the state-space approach have been proposed to represent and reason out actions. Considering these aspects, this paper provides an account of work from the viewpoint of linguistics, logic and action representation formalisms. Based on this study, this paper then proposes a seven axes categorization scheme, that can be used to compare and analyze different theories.     

智能体对手建模研究进展

智能体是人工智能领域的一个核心术语。近年来,智能体技术在自动无人驾驶、机器人系统、 电子商务、传感网络、智能游戏等方面得到了广泛研究与应用。随着系统复杂性的增加,关于智能体的研究重 心由对单个智能体的研究转变为智能体间交互的研究。多个智能体交互场景中,智能体对其他智能体决策行为 的推理能力是非常重要的一个方面,通常可以通过构建参与交互的其他智能体的模型,即对手建模来实现。对 手建模有助于对其他智能体的动作、目标、信念等进行推理、分析和预测,进而实现决策优化。为此,重点关 注智能体对手建模研究,展开介绍关于智能体动作预测、偏好预测、信念预测、类型预测等方面的对手建模 技术,对其中的优缺点进行讨论和分析,并对手建模技术当前面临的一些开放问题进行总结,探讨未来可能 的研究和发展方向。     

基于精确主轴分析及ICP的三维人脸配准

论文提出了一种基于精确主轴分析的三维人脸预配准方法。利用人脸在其主轴坐标系下均呈相同的正面姿态的特点,通过将标准模型和待配准模型都转换到其主轴坐标系下,来实现三维人脸模型的预配准,然后使用ICP方法进行精确配准。实验证明,所提方法具有良好的姿态鲁棒性和噪声鲁棒性。     

利用分布式主动智能体检测灰度图像的对称轴

图像的对称性研究是模式识别和计算几何的重要内容,在目标识别、视觉监视和形状表示等研究中有一定的应用价值.提出一种利用分布式主动智能体提取灰度图像中的基本反射对称轴的新方法,它通过对智能体在局部图像环境中定居、发展以及迁徙和死亡等行为的模拟来检测、编组和连接显著的局部对称轴.该方法可以提取任意灰度图像中的基本反射对称轴,并且易于并行实现,关于自然图像的实验结果表明了它的有效性.