Trust is a fundamental concept in many real-world applications such as e-commerce and peer-to-peer networks. In these applications, users can generate local opinions about the counterparts based on direct experiences, and these opinions can then be aggregated to build trust among unknown users. The mechanism to build new trust relationships based on existing ones is referred to as trust inference. State-of-the-art trust inference approaches employ the transitivity property of trust by propagating trust along connected users. In this paper, we propose a novel trust inference model (MaTrust) by exploring an equally important property of trust, i.e., the multi-aspect property. MaTrust directly characterizes multiple latent factors for each trustor and trustee from the locally-generated trust relationships. Furthermore, it can naturally incorporate prior knowledge as specified factors. These factors in turn serve as the basis to infer the unseen trustworthiness scores. Experimental evaluations on real data sets show that the proposed MaTrust significantly outperforms several benchmark trust inference models in both effectiveness and efficiency.

AntBeePath is a hybrid bio-inspired algorithm based on the behavior of ants and honeybees aimed at the resolution of the problem of finding the shortest paths for a given network topology. The algorithm, in brief, combines the pheromone release mechanism of existing Ant Colony Optimization (ACO) algorithms with a new bio-inspired mechanism based on the recruitment strategy of bees. Three versions of the algorithm were developed incrementally. Proof-of-concept results indicate that the AntBeePath Decay Hybrid Chain version is more efficient than the other developed versions and, beyond that, presented an improved performance in relation to an equivalent ACO algorithm. The results suggest that a hybrid algorithm, combining the ant’s pheromone release with the new bio-inspired mechanism of bee recruitment along with a stagnation control mechanism can result in a new bio-inspired algorithm for path determination with improved characteristics.

In this position paper, we synthesize "within the system" models of human influence over bio-inspired swarms, summarizing observations from previous experiments and identifying methods of influence that have not yet been explored. We describe (a) differences among agents that can be controlled by a human and those that can't, (b) agents that are aware of the type of other agents and those that aren't, and (c) the effects of attraction, repulsion, and orientation on human-guided swarm behavior. We also briefly discuss the interaction effort required to manage swarms.

Although decision processes are often described at the individual level of cognition (e.g. Tversky and Kahnemann The particular beliefs instantiated within the model are (1981)), they are subject to social and cultural influences based on a combination of results from empirical studies at both the interpersonal and societal levels. The adoption of technology adoption by Venkatesh et al. (2003). of new technology depends on various factors, such The UTAUT model combines eight of the most prominent as the type of technology, the context or culture in which technology-acceptance models observed in the literature and the technology is introduced, and the individual decisions provides a definitive list of variables that are critically relevant by people within that culture, as most individuals evaluate to an individual's Behavioral Intention (BI) and Use Behavior an innovation from the subjective evaluations of peers who (UB) for adopting a new technology, including Performance have adopted an innovation (see Watts and Dodds (2007) Expectancy (PE), Effort Expectancy (EE), Social for a discussion of network-diffused influence). These influences Influence (SI), Facilitating Conditions (FC), and Voluntariness propagate through the social network as a function of Use (VoU). of agent interactions.

Many interesting real-world tasks might be most efficiently, effectively and safely achieved with large teams of robots working together. For domains such as the military, rescue response and environmental monitoring, the ability for the team to be spread out in the environment collecting information and taking action is a key enabler. Over an extended period of time, we have developed an infrastructure that can be quickly implemented on a robot or software agent to allow that agent to become part of a team. That infrastructure, called Machinetta, works by implementing a theory of teamwork that knows how to execute Team Oriented Plans. The infrastructure understands how to allocate roles, share information, recover from failures and other routine coordination activities that do not need to be specified in the plan. In most applications of Machinetta, invocation of Team Oriented Plans is the mechanism by which the operator interacts with the team, letting them specify the team activities without worrying about low-level details. Recently we have extended the team oriented plan concept to include situational awareness and mixed initiative markup that tells the GUI what information and options to give to the operator at different points during plan execution. In recent experiments with teams of boats, we have begun including swarming behaviors as a part of the team plan, when useful. The innvocation of swarming behavior from within Team Oriented Plans, offers a new way of interacting with very large robotic teams.

In the current generation The main results issued from our first experiments (Legras of UV Systems, several ground operators operate a single et al. 2008; Coppin and Legras 2012) were that the swarm vehicle with limited autonomous capabilities, whereas, approach seemed to be robust and adapted for simple mission in the next generation of UV Systems, a ground operator of surveillance, but that the operators in charge of will have to supervise a system of several cooperating vehicles such a system were not ready to understand and dialog with performing a joint mission, i.e. a Multi-Agent System this new kind of system, so that the global performance of (MAS) (Johnson 2003; Coppin and Legras 2012). In order the system was potentially spoiled by human intervention.

There have been significant advances in developing each phase of the mission. Secondly, based on our everyday algorithms that allow researchers to examine these experience with physical objects in our environment, behaviors in simulation (Luke et al. 2005), generally assuming the three major physical states of matter, solid, liquid and noise-free estimates of the agents' own, neighbors' and gas, represent a natural and intuitive means of describing the targets' positions. However, the actual information flow into types of motions a swarm of mobile robots can perform as biological agents' in terms of the sensing, processing and they cluster, transit or wander (Gage 1992).

Statistical models almost always yield predictions that are more accurate than those of human experts. However, humans are better at data acquisition and at recognizing atypical circumstances. We use prediction markets to combine predictions from groups of humans and artificial-intelligence agents and show that they are more robust than those from groups of humans or agents alone.

Human control of vehicle swarms faces a dilemma: an operator must be able to exercise precise control over how a mission is executed, but controlling individual vehicles is not scalable. The Proto spatial computing lan- guage offers an intermediate representation, where the motion of a swarm is specified as a vector field, which is then approximated by the movement of individual members (Bachrach, Beal, and McLurkin 2010). I propose that this can be exploited to build a “tactical command” model of swarm control, whereby human “officers” dynamically decompose a swarm into units and task those units to carry out geometric and topological maneuvers under the constraints imposed by the platform. This abstraction may also allow situation awareness interfaces for individual agents to be extended to apply to swarm units.

In the real world we observe a proliferation of regional dialects and jargons. Most of the research on naming conventions focuses on how to explain the process that allows a single naming convention to establish itself. This paper presents a different approach that aims to investigate why different conventions may emerge and coexist for a certain amount of time. The naming game is an abstraction of lexical acquisition dynamics, in which n agents try to find an agreement on the names to give to objects. To understand how different heterogeneous conventions emerge, I discuss a naming game model that takes into account experimental data on human and animal learning.