Real-time optimization of the dynamic flow of vehicle traffic through a network of signalized intersections is an important practical problem. In this paper, we take a decentralized, schedule-driven coordination approach to address the challenge of achieving scalable network-wide optimization. To be locally effective, each intersection is controlled independently by an on-line scheduling agent. At each decision point, an agent constructs a schedule that optimizes movement of the observable traffic through the intersection, and uses this schedule to determine the best control action to take over the current look-ahead horizon. Decentralized coordination mechanisms, limited to interaction among direct neighbors to ensure scalability, are then layered on top of these asynchronously operating scheduling agents to promote overall performance. As a basic protocol, each agent queries for newly planned output flows from its upstream neighbors to obtain an optimistic projection of future demand. This projection may incorporate non-local influence from indirect neighbors depending on horizon length. Two additional mechanisms are then introduced to dampen ``nervousness'' and dynamic instability in the network, by adjusting locally determined schedules to better align with those of neighbors. We present simulation results on two traffic networks of tightly-coupled intersections that demonstrate the ability of our approach to establish traffic flows with lower average vehicle wait times than both a simple isolated control strategy and other contemporary coordinated control strategies that use moving average forecast or traditional offset calculation.

Mapping and tracking biological ocean features, such as harmful algal blooms, is an important problem in the environmental sciences. The problem exhibits a high degree of uncertainty, because of both the dynamic ocean context and the challenges of sensing. Plan-based policy learning has been shown to be a powerful technique for obtaining robust intelligent behaviour in the face of uncertainty. In this paper we apply this technique in simulation, to the problem of tracking the outer edge of 2D biological features, such as the surfaces of harmful algal blooms. We show that plan-based policy-learning leads to highly accurate tracking in simulation, even in situations where the uncertainty governing the shape of the patch cannot be directly modelled. We present simulation results that give confidence that the approach could work in practice. We are now collaborating with ocean scientists at MBARI to perform physical tests at sea.

Social causality is the inference an entity makes about the social behavior of other entities and self. Besides physical cause and effect, social causality involves reasoning about epistemic states of agents and coercive circumstances. Based on such inference, responsibility judgment is the process whereby one singles out individuals to assign responsibility, credit or blame for multi-agent activities. Social causality and responsibility judgment are a key aspect of social intelligence, and a model for them facilitates the design and development of a variety of multi-agent interactive systems. Based on psychological attribution theory, this paper presents a domain-independent computational model to automate social inference and judgment process according to an agents causal knowledge and observations of interaction. We conduct experimental studies to empirically validate the computational model. The experimental results show that our model predicts human judgments of social attributions and makes inferences consistent with what most people do in their judgments. Therefore, the proposed model can be generically incorporated into an intelligent system to augment its social and cognitive functionality.

Determining the position of a robot in an environment, termed localization, is one of the challenges facing roboticist. Localization is essential to solving more complex problems such as locomotion, path planning and environmental learning. Our lab is developing a multi-agent system to use multiple small robots to accomplish tasks normally completed by larger robots. However, because of the reduced size of these robots, methods previously used to determine the position of the robot, such as GPS, cannot be employed. The problem we are facing is that we need to be able to determine the position of each of the robots in this multi-agent system simultaneously. We have developed a system to help track and identify robots using an overhead camera and LEDs, mounted on the robots, to efficiently solve the localization problem.

Biologists seek concise, testable models of behavior for the animals they study. We suggest a robot programming paradigm in which animal behaviors are described as robot controllers to support a cycle of hypothesis generation and testing of animal models. In this work we illustrate that approach by modeling the hunting behavior of a captive colony of Aphaenogaster cockerelli , a desert harvester ant. In laboratory animal experiments we introduce live prey (fruit flies) into the foraging arena of the colony. We observe the behavior of the ants, and we measure aspects of their performance in capturing the prey. Based on these observations we create a model of their behavior using Clay, a Java library developed for coding hybrid controllers in a behavior-based manner. We then validate that model in quantitative comparisons with the live animal behavior.

We model the pursuit problem as a set of distributed agents communicating over a network subject to latency. Latency has serious deleterious effects on solving the pursuit problem. In this paper, we present a simple, yet effective way of dealing with latency that yields very good performance. Our method disperses predators within a region in which the prey may move that accounts for network latency.

Social influence has no small effect on the preferences and behavior of agents in a social space. Contrary to rationality, we sometimes compromise our own needs for those of others. Thus, social influence has important implications in agent cognitive modeling for multi-objective decision-making problems. Namely, where these activities occur within a social context, the intentional preferences or utility of an agent may be subsumed, to a greater or lesser degree, by the influences of other agents. In this paper, a socially-aware model predictive controller is proposed using a social influence network theory and applied to a HVAC control problem. It transforms individual agent utility to socially-influenced utility reflecting interagent influences due to their existing relationships.

This project focuses on artificial social interactions where things get nasty and mean. The purpose is training in social 'facework' -- managing the situation so that participants maintain their social dignity or 'face'. This can be especially delicate in cross-cultural contexts, where assumptions about social protocols and the emotional associations of utterances and gestures may differ. The purpose of this project is two-fold. First, it is intended as a training system, so that users might learn the do's and don'ts of social interactions in different cultures and different situations. The knowledge base draws from existing theories of diplomacy, facework, and (im)politeness theory. The other goal is to provide a platform for observation and experimentation of social interaction in an artificial, virtual setting in order to improve these theories.

Human decision making is a complex process. In the field of Artificial Intelligence, decision making is considered an essential aspect of autonomous agents. Research of human decision behaviour shows that emotions play a decisive role. We present a computational model for creating an emotional memory and an algorithm for decision making based on the collected information in the memory. We concentrate on simulating human behaviour as there is not always one perfect way to reach a goal but alternatives that are more advantageous. For evaluation purposes a gambling task, performed by real subjects, was created for the modelled agent. The results show that the decision behaviour of the modelled agent is comparable with real subjects.

The Association for the Advancement of Artificial Intelligence was pleased to present the 2011 Fall Symposium Series, held Friday through Sunday, November 4–6, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the seven symposia are as follows: (1) Advances in Cognitive Systems; (2) Building Representations of Common Ground with Intelligent Agents; (3) Complex Adaptive Systems: Energy, Information and Intelligence; (4) Multiagent Coordination under Uncertainty; (5) Open Government Knowledge: AI Opportunities and Challenges; (6) Question Generation; and (7) Robot-Human Teamwork in Dynamic Adverse Environment. The highlights of each symposium are presented in this report.