We introduce the notion of strategic uncertainty for boundedly rational, non-myopic agents as an analog to the equilibrium selection problem in classical game theory. We then motivate the need for and feasibility of addressing strategic uncertainty and present an algorithm that produces decisions that are robust to it. Finally, we show how agents' rationality levels and planning horizons alter the robustness of their decisions.

HAPTIC is individuals - agents or humans - within them to establish grounded in game theory and probabilistic modeling. It has successful relationships with their partners. In Supply been proved that HAPTIC agents learn other agents' behaviors Chain Management (SCM), establishing trust improves the reliably using direct observations. One shortcoming of chances of a successful supply chain relationship, and increases HAPTIC is that it does not support reported observations.

Learning by demonstration technology has long held the promise to empower non-programmers to customize and extend software. We describe the deployment of a learning by demonstration capability to support user creation of automated procedures in a collaborative planning environment that is used widely by the U.S. Army. This technology, which has been in operational use since the summer of 2010, has helped to reduce user workloads by automating repetitive and time-consuming tasks. The technology has also provided the unexpected benefit of enabling standardization of products and processes. 

Human error is one of the most common causes of vulnerability in asecure system. However it is often overlooked when these systems aretested, partly because human tests are costly and very hard torepeat. We have developed a community of agents that test securesystems by running standard windows software while performingcollaborative group tasks, mimicking more realistic patterns ofcommunication and traffic, as well as human fatigue and errors. Thissystem is being deployed on a large cyber testing range. One keyattribute of humans is flexibility of response in order to achievetheir goals when unexpected events occur. Our agents use reactiveplanning within a BDI architecture to flexibly re-plan if needed.Since the agents are goal-oriented, we are able to measure the impactof cyber attacks on mission accomplishment, a more salient measure ofprotection than raw penetration. We show experimentally how the agentteams can be resilient under attacks that are partly successful, andalso how an organizational structure can lead to emergent propertiesof the traffic in the network.

Robots with many sensors are capable of generating volumes of high-dimensional perceptual data. Making sense of this data and extracting useful knowledge from it is a difficult problem. For robots lacking proper models, trying to understand a stream of uninterpreted data is an especially acute problem. One critical step in linking raw uninterpreted perceptual data to cognition is dimensionality reduction. Current methods for reducing the dimension of data do not meet the demands of a robot situated in the world, and methods that use only perceptual data do not take full advantage of the interactive experience of an embodied robot agent. This work proposes a new scalable, incremental and active approach to dimensionality reduction suitable for extracting geometric knowledge from uninterpreted sensors and effectors. The proposed method uses distinctive state abstractions to organize early sensorimotor experience and sensorimotor embedding to incrementally learn accurate geometric representations based on experience. This approach is applied to the problem of learning the geometry of sensors, space, and objects. The result is evaluated using techniques from statistical shape analysis.

The goal of my research is to develop and evaluate long-term declarative memory mechanisms that are effective and efficient across a variety of tasks.

The focus of my research is an agent-based system for optimizing spatial arrangements of plants on a landscape to maximize their growth and minimize their water use. The optimization criteria include a natural phenomenon known as facilitation, which is observed in water-scarce environments when larger shrubs serve as benefactors to smaller annuals by generating conditions that protect them from harsh afternoon sun. In my modeling and optimization system each plant is an agent with growth requirements. A plant agent's fitness at a given location is defined by a fitness function that includes those growth requirements and a penalty term designed to force facilitation. The landscape design is formulated as a combinatorial optimization problem with a discrete set of locations for each plant on a grid, a fixed number of plants, and a fitness function that defines the performance of a plant at a location. To evaluate the effectiveness of this approach, I applied a variety of search strategies, including simulated annealing and a new agent-based approach that mimics how plant communities evolve over time, to different collections of simulated plant types and landscapes and compared the fitness scores and spatial arrangments in the solutions. The fitness scores from the search strategies were comparable. The search strategies produced different spatial distributions of the larger plants, and all designs exhibited facilitation and lower water use.

We introduce a Java graphical gaming framework that enables students in an introductory artificial intelligence (AI) course to immediately apply and visualize the topics from class. We have used this framework in teaching a mixed undergraduate/graduate AI course for six years. We believe that the use of games motivates students. The graphical nature of each game enables students to quickly see how well their algorithm works. Because the topics in an introductory AI course vary widely, students apply their algorithms to multiple game environments. A final challenging environment enables them to tie together the concepts for the entire semester.

The undergraduate computer science curriculum is generally focused on skills and tools;  most students are not exposed to much  research in the field, and do not learn how to navigate the research literature.  We describe how science fiction reviews were used as a gateway to research reviews.  Students learn a little about current or recent research on a topic that stirs their imagination, and learn how to search for, read critically, and compare technical papers on a topic related their chosen science fiction book, movie, or TV show.

There are many diverse domains like academic collaboration, service industry, and movies, where a group of agents are involved in a set of activities through interactions or collaborations to create value. The end result of the value creation process is two pronged: firstly, there is a cumulative value created due to the interactions and secondly, a network that captures the pattern of historical interactions between the agents. In this paper we summarize our efforts towards design and analysis of value creation networks: 1) network representation of interactions and value creations, 2) identify contribution of a node based on values created from various activities, and 3) ranking nodes based on structural properties of interactions and the resulting values. To highlight the efficacy of our proposed algorithms, we present results on IMDB and services industry data.