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.

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.

Members of a multi-robot team, operating within close quarters, need to avoid crashing into each other. Simple collision avoidance methods can be used to prevent such collisions, typically by computing the distance to other robots and stopping, perhaps moving away, when this distance falls below a certain threshold. While this approach may avoid disaster, it may also reduce the team's efficiency if robots halt for a long time to let others pass by or if they travel further to move around one another. This paper reports on experiments where a human operator, through a graphical user interface, watches robots perform an exploration task. The operator can manually suspend robots' movements before they crash into each other, and then resume their movements when their paths are clear. Experiment logs record the robots' states when they are paused and resumed. A behavior pattern for collision avoidance is learned, by classifying the states of the robots' environment when the human operator issues "wait" and "resume" commands. Preliminary results indicate that it is possible to learn a classifier which models these behavior patterns, and that different human operators consider different factors when making decisions about stopping and starting robots.

Intelligent systems promise to amplify, augment, and extend innate human abilities. A principal example is that of assistive rehabilitation robots---artificial intelligence and machine learning enable new electromechanical systems that restore biological functions lost through injury or illness. In order for an intelligent machine to assist a human user, it must be possible for a human to communicate their intentions and preferences to their non-human counterpart. While there are a number of techniques that a human can use to direct a machine learning system, most research to date has focused on the contrasting strategies of instruction and reward. The primary contribution of our work is to demonstrate that the middle ground between instruction and reward is a fertile space for research and immediate technological progress. To support this idea, we introduce the setting of human-prompted switching, and illustrate the successful combination of switching with interactive learning using a concrete real-world example: human control of a multi-joint robot arm. We believe techniques that fall between the domains of instruction and reward are complementary to existing approaches, and will open up new lines of rapid progress for interactive human training of machine learning systems.

Learning from demonstration (LfD) is a promising technique for instructing/teaching autonomous systems based on demonstrations from people who may have little to no experience with robots. An important aspect to LfD is the communication method used to transfer knowledge from an instructor to a robot. The communication method affects the complexity of the demonstration process for instructors, the range of tasks a robot can learn, and the learning algorithm itself. We have designed a graphical interface and an instructional language to provide an intuitive teaching system. The drawback to simplifying the teaching interface is that the resulting demonstration data are less structured, adding complexity to the learning process. This additional complexity is handled through the combination of a minimal set of predefined behaviors and a task representation capable of learning probabilistic policies over a set of behaviors. The predefined behaviors consist of finite actions a robot can perform, which act as building blocks for more complex tasks.

One common use for swarming agents is in social simulation. This paper reports on such a model developed to track protest activities at the May 2012 NATO summit in Chicago. The use of apoptotic stigmergic agents allows the model to run on-line, consuming two kinds of external data and reporting its results in real time.

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.

This paper provides a comparison between alternate styles and tecnhiques for controlling many subordinate agents: delegation vs. swarm "control" or influence. Each management style is defined and pros and cons articulated. The author then attempts to apply a model he created in prior work of the "tradeoff space" of automation control approaches along three dimensions: competence, workload and unpredictability. This application offers insights about the strengths and weaknesses of each approach, but also points to a limitation in the characterization of the tradeoff space.

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.