Corruptible social agents

Agent‐based virtual simulations of social systems susceptible to corruption (e.g., police agencies) require agents capable of exhibiting corruptible behaviors to achieve realistic simulations and enable the analysis of corruption as a social problem. This paper proposes a formal belief‐desire‐intention framework supported by the functional event calculus and fuzzy logic for modeling corruption based on the integrity level of social agents and the influence of corrupters on them. Corruptible social agents are endowed with beliefs, desires, intentions, and corrupt‐prone plans to achieve their desires. This paper also proposes a fuzzy logic system to define the level of impact of corruption‐related events on the degree of belief in the truth of anti‐corruption factors (e.g., the integrity of the leader of an organization). Moreover, an agent‐based model of corruption supported by the proposed belief‐desire‐intention framework and the fuzzy logic system was devised and implemented. Results obtained from agent‐based simulations are consistent with actual macro‐level patterns of corruption reported in the literature. The simulation results show that (i) the bribery rate increases as more external entities attempt to bribe agents and (ii) the more anti‐corruption factors agents believe to be true, the less prone to perpetrate acts of corruption. Copyright © 2014 John Wiley & Sons, Ltd.     

Ethics and consciousness in artificial agents

In what ways should we include future humanoid robots, and other kinds of artificial agents, in our moral universe? We consider the Organic view, which maintains that artificial humanoid agents, based on current computational technologies, could not count as full-blooded moral agents, nor as appropriate targets of intrinsic moral concern. On this view, artificial humanoids lack certain key properties of biological organisms, which preclude them from having full moral status. Computationally controlled systems, however advanced in their cognitive or informational capacities, are, it is proposed, unlikely to possess sentience and hence will fail to be able to exercise the kind of empathic rationality that is a prerequisite for being a moral agent. The organic view also argues that sentience and teleology require biologically based forms of self-organization and autonomous self-maintenance. The organic view may not be correct, but at least it needs to be taken seriously in the future development of the field of Machine Ethics.     

Virtuous vs. utilitarian artificial moral agents

AI & SOCIETY - Given that artificial moral agents—such as autonomous vehicles, lethal autonomous weapons, and automated trading systems—are now part of the socio-ethical equation,...     

A social approach for learning agents

In this paper, we propose a social approach for learning agents. In dynamic environments, smart agents should detect changes and adapt themselves, applying dynamic learning strategies and drift detection algorithms. Recent studies note that an ensemble of learners can be coordinated by simple protocols based on votes or weighted votes; however, they are not capable of determining the number of learners or the ensemble composition properly. Conversely, we show in this paper that Social Network Theory can provide the multi-agent learning community with sophisticated and well-founded reputation models that outperform well-known ensemble-based drift detection techniques, generating accurate and small ensembles of learning agents. Our approach is evaluated considering dynamic bilateral negotiation scenarios and benchmark databases, presenting statistically significant results that are better than those of other ensemble-based techniques.     

Using cognitive agents in social simulations

Multi-Agent-Based Social Simulation (MABS) is a paradigm devoted to using agents as the modelling metaphor to simulate autonomous entities in a social world composed of a number of independent and interacting entities. Such models try to reproduce real environments and situations of interest within such environments. Most MABS platforms used today (e.g. MASON, Repast, NetLogo) see agents as very simple entities. However, there are situations in which a more intelligent kind of agent is needed. For example, when a society of persons with different roles and high-level behaviours must be modelled. In this paper, we address how to incorporate agents with cognitive skills into MABS.     

Modeling social inference in virtual agents

Social judgment is a social inference process whereby an agent singles out individuals to blame or credit for multi-agent activities. Such inferences are a key aspect of social intelligence that underlie social planning, social learning, natural language pragmatics and computational models of emotion. With the advance of multi-agent interactive systems and the need of designing socially aware systems and interfaces to interact with people, it is increasingly important to model this human-centric form of social inference. Based on psychological attribution theory, this paper presents a general computational framework to automate social inference based on an agent’s causal knowledge and observations of interaction.     

Evolution of visually guided behavior in artificial agents

Recent work on brightness, color, and form has suggested that human visual percepts represent the probable sources of retinal images rather than stimulus features as such. Here we investigate the plausibility of this empirical concept of vision by allowing autonomous agents to evolve in virtual environments based solely on the relative success of their behavior. The responses of evolved agents to visual stimuli indicate that fitness improves as the neural network control systems gradually incorporate the statistical relationship between projected images and behavior appropriate to the sources of the inherently ambiguous images. These results: (1) demonstrate the merits of a wholly empirical strategy of animal vision as a means of contending with the inverse optics problem; (2) argue that the information incorporated into biological visual processing circuitry is the relationship between images and their probable sources; and (3) suggest why human percepts do not map neatly onto physical reality.     

A high-level architecture for believable social agents

The creation of virtual humans capable of behaving and interacting realistically with each other requires the development of autonomous believable social agents. Standard goal-oriented approaches are not well suited to it because they don't take into account important characteristics identified by the social sciences. This paper tackles the issue of a general social reasoning mechanism, discussing its basic functional requirements using a sociological perspective, and proposing a high-level architecture based on Roles, Norms, Values and Types.     

Using trust for detecting deceitful agents in artificial societies

Trust is one of the most important concepts guiding decision-making and contracting in human societies. In artificial societies, this concept has been neglected until recently. The inherent benevolence assumption implemented in many multiagent systems can have hazardous consequences when dealing with deceit in open systems. The aim of this paper is to establish a mechanism that helps agents to cope with environments inhabited by both selfish and cooperative entities. This is achieved by enabling agents to evaluate trust in others. A formalization and an algorithm for trust are presented so that agents can autonomously deal with deception and identify trustworthy parties in open systems. The approach is twofold: agents can observe the behavior of others and thus collect information for establishing an initial trust model. In order to adapt quickly to a new or rapidly changing environment, one enables agents to also make use of observations from other agents. The practical relevance of these ideas is demonstrated by means of a direct mapping from a scenario to electronic commerce.     

Emergence of memory-driven command neurons in evolved artificial agents

Using evolutionary simulations, we develop autonomous agents controlled by artificial neural networks (ANNs). In simple lifelike tasks of foraging and navigation, high performance levels are attained by agents equipped with fully recurrent ANN controllers. In a set of experiments sharing the same behavioral task but differing in the sensory input available to the agents, we find a common structure of a command neuron switching the dynamics of the network between radically different behavioral modes. When sensory position information is available, the command neuron reflects a map of the environment, acting as a location-dependent cell sensitive to the location and orientation of the agent. When such information is unavailable, the command neuron's activity is based on a spontaneously evolving short-term memory mechanism, which underlies its apparent place-sensitive activity. A two-parameter stochastic model for this memory mechanism is proposed. We show that the parameter values emerging from the evolutionary simulations are near optimal; evolution takes advantage of seemingly harmful features of the environment to maximize the agent's foraging efficiency. The accessibility of evolved ANNs for a detailed inspection, together with the resemblance of some of the results to known findings from neurobiology, places evolved ANNs as an excellent candidate model for the study of structure and function relationship in complex nervous systems.     

GRASP agents: social first,intelligent later

This paper urges that if we wish to give social intelligence to our agents, it pays to look at how we acquired our social intelligence ourselves. We are born with drives and motives that are innate and deeply social. Next, as children we are socialized to acquire norms and values and to understand rituals large and small. These social elements are the core of our being. We capture them in the acronym GRASP: Groups, Rituals, Affiliation, Status, Power. As a consequence, economic rationality or logical reasoning do not suffice when it comes to social intelligence. Basic features of our social behaviour, of the kind that one sees early in the lives of children, need to be prominent. These include fear, love, and aggression. They extend to the combination of these drives with basic social clues from the environment such as big and small, good and bad, as well as culture-specific specializations of these. This will make agents respond differentially to inferred attributes such as gender, age, group membership. This level of universality in social intelligence should receive our full attention. The general insights gained can then be re-used in myriad implementations to specific modelling issues.

     

Fostering artificial societies using social learning and social control in parallel emergency management systems

How can we foster and grow artificial societies so as to cause social properties to emerge that are logical, consistent with real societies, and are expected by designers? We propose a framework for fostering artificial societies using social learning mechanisms and social control approaches. We present the application of fostering artificial societies in parallel emergency management systems. Then we discuss social learning mechanisms in artificial societies, including observational learning, reinforcement learning, imitation learning, and advice-based learning. Furthermore, we discuss social control approaches, including social norms, social policies, social reputations, social commitments, and sanctions.     

Teams of intelligent agents which learn using artificial immune systems

We consider the problem of having an agent respond appropriately to dynamically changing environments. We work within the Beliefs–Desires–Intentions paradigm and show how an agent may use concepts suggested by Artificial Immune Systems to dynamically change its intentions in response to a dynamically changing environment. We illustrate these ideas with teams of agents who must either compete or cooperate in the context of simple artificial games in order to fulfil their specific desires.     

APES: Attentively Perceiving Robot

Robot vision systems—inspired by human-like vision—are required to employ mechanisms similar to those that have proven to be crucial in human visual performance. One of these mechanisms is attentive perception. Findings from vision science research suggest that attentive perception requires a multitude of properties: A retina with fovea-periphery distinction, an attention mechanism that can be manipulated both mechanically and internally, an extensive set of visual primitives that enable different representation modes, an integration mechanism that can infer the appropriate visual information in spite of eye, head, body and target motion, and finally memory for guiding eye movements and modeling the environment. In this paper we present an attentively “perceiving” robot called APES. The novelty of this system stems from the fact that it incorporates all of these properties simultaneously. As is explained, original approaches have to be taken to realize each of the properties so that they can be integrated together in an attentive perception framework.     

Models of opinion control for agents in social networks

A class of optimization control problems is considered in social networks under certain constraints imposed on opinions of agents. Well-known solution methods are discussed. The corresponding mathematical models are proposed and their specific features are identified. Finally, the directions and restrictions on their possible application in practice are analyzed.     

基于社会学习机制的改进人工鱼群算法

针对人工鱼群算法后期搜索速度慢、不易得到精确解等问题,结合社会学习机制提出一种改进算法。当人工鱼群算法进行到优化后期时,使用群体社会学习机制中的趋同和趋异行为进行寻优。两种行为搜索速度快,寻优精度高,且趋异现象提高了群体的多样性,增强了跳出局部极值的能力,在一定程度上改善了原算法的搜索性能。仿真实验结果表明了改进算法的可行性和有效性。     

Perceiving spatial relationships in computer-generated images

The sources of visual information that must be present to correctly interpret spatial relations in images, the relative importance of different visual information sources with regard to metric judgments of spatial relations in images, and the ways that the task in which the images are used affect the visual information's usefulness are discussed. Cue theory, which states that the visual system computes the distances of objects in the environment based on information from the posture of the eyes and from the patterns of light projected onto the retinas by the environment, is presented. Three experiments in which the influence of pictorial cues on perceived spatial relations in computer-generated images was assessed are discussed. Each experiment examined the accuracy with which subjects matched the position, orientation, and size of a test object with a standard by interactively translating, rotating, and scaling the test object     

Perceiving interpersonally-mediated risk in virtual environments

Using virtual reality (VR) to examine risky behavior that is mediated by interpersonal contact, such as agreeing to have sex, drink, or smoke with someone, offers particular promise and challenges. Social contextual stimuli that might trigger impulsive responses can be carefully controlled in virtual environments (VE), and yet manipulations of risk might be implausible to participants if they do not feel sufficiently immersed in the environment. The current study examined whether individuals can display adequate evidence of presence in a VE that involved potential interpersonally-induced risk: meeting a potential dating partner. Results offered some evidence for the potential of VR for the study of such interpersonal risk situations. Participants’ reaction to the scenario and risk-associated responses to the situation suggested that the embodied nature of virtual reality override the reality of the risk’s impossibility, allowing participants to experience adequate situational embedding, or presence.