Robust methods for representing, generalizing, and sharing knowledge across various robotics systems and configurations are important in many domains of robotics research and application. In this paper we present a method for modeling tasks and robot skills to simplify the programming and reuse of knowledge between robots in manufacturing environments. Specifically, we propose an assembly taxonomy designed to represent the decomposition of high-level, complex assembly tasks into simple skills and skill primitives that the robot must use in a specified sequence. By using programming by demonstration to populate the taxonomy, we propose a method to easily interact with and reuse knowledge in various manufacturing robotics systems, making it possible to reduce programming time and overhead. We present both a detailed discussion of this taxonomy, as well as an example of how the taxonomy can be applied to an assembly task.

A robot coexisting with humans must not only be able to successfully perform physical tasks, but must also be able to interact with humans in a socially appropriate manner. In many social settings, this involves the use of social signals like gaze, facial expression, and language. In this paper we discuss preliminary work focusing on the problem of combining social interaction with task-based action in a dynamic, multiagent bartending domain, using an embodied robot. We discuss how social states are inferred from low-level sensors, using vision and speech as input modalities, and present a planning approach that models task, dialogue, and social actions in a simple bartending scenario. This approach allows us to build interesting plans, which have been evaluated in a real-world study with human subjects, using a general purpose, off-the-shelf planner, as an alternative to more mainstream methods of interaction management.

For the long term co-existence of robots with us in complete harmony, they will be expected to show sociocognitive behaviors. In this paper, taking inspiration from child development research and human behavioral psychology we will identify the basic but key capabilities: perceiving abilities, effort and affordances. Further we will present the concepts, which fuse these components to perform multi-effort ability and affordance analysis. We will show instantiations of these capabilities on real robot and will discuss its potential applications for more complex socio-cognitive behavior.

Many tasks for autonomous agents or robots are best de- scribed by a specification of the environment and a specifi- cation of the available actions the agent or robot can perform. Combining such a specification with the possibility to imper- atively program a robot or agent is what we call the action- based imperative programming. One of the most successful such approaches is Golog. In this paper, we draft a proposal for a new robot program- ming language YAGI, which is based on the action-based imperative programming paradigm. Our goal is to design a small, portable stand-alone YAGI interpreter. We combine the benefits of a principled domain specification with a clean, small and simple programming language, which does not ex- ploit any side-effects from the implementation language. We discuss general requirements of action-based programming languages and outline YAGI, our action-based language ap- proach which particularly aims at embeddability.

In recent years, major research advances have taken place in the intersection of computer science and economics, but this material has so far been taught primarily at the graduate level. This paper describes a novel semester-long undergraduate-level course in the intersection of computer science and economics at Duke University, titled “CPS 173: Computational Microeconomics.”

This research proposes the use of imitation based learning to build collaborative strategies for a team of agents. Imitation based learning involves learning from an expert by observing her demonstrating a task and then replicating it. This mechanism makes it extremely easy for a knowledge engineer to transfer knowledge to a software agent via human demonstrations. This research aims to apply imitation to learn not only the strategy of an individual agent but also the collaborative strategy of a team of agents to achieve a common goal. The effectiveness of the proposed methodology is being assessed in the domain of RoboCup Soccer Simulation 3D which is a promising platform to address many of the complex real-world problems and offers a truly dynamic, stochastic, and partially-observable environment.

Prior work done in learning by instruction (Huffman and Laird, 1995) Learning by interacting with humans is a powerful learning demonstrated learning systems that focus on agent-initiated paradigm. In a complex world learning through self-directed interaction, where instruction is directed by impasses arising experience alone can be slow, requiring repeated interactions in a Soar agent. They noted that instructor-initiated interaction with the environment. Learning from human-agent interaction is difficult to support because of the likely interruption can reduce the complexity of the learning task by reducing of agent's reasoning.

Multiagent resource allocation under uncertainty raises various computational challenges in terms of efficiency such as intractability, communication cost, and preference representation. To date most approaches do not provide efficient solutions for dynamic environments where temporal constraints pose particular challenges. We propose two techniques to cope with such settings: auctions to allocate fairly according to preferences, and MDPs to address stochasticity. This research seeks to determine the ideal combination between the two methods to handle wide range of allocation problems with reduced computation and communication cost between agents.

Groups of robots are often able to to accomplish missions that no single robot can achieve by themselves. Teamwork is a very important factor in complex, dynamic domains. In heterogeneous teams, robustness and flexibility are increased by the diversity of the robots, each contributing different capabilities. In such heterogeneous Multi-Robot Systems it is reasonable to explicitly take the robots' capabilities into account when determining which one is best suited for a task. In this paper I present a framework that formalizes robots' capabilities and provides a means to estimate their suitability for a task. In highly unpredictable domains, accurate predictions of the outcomes of a robot's actions are virtually impossible. Approximate models and algorithms are required which help to estimate the outcome with highest possible confidence. The proposed architecture can provide estimates of task solution qualities at three levels of confidence: the lowest level only taking the mere existence of capabilities into account, the middle level considering task-specific details with approximate parameters of the capabilities, and the highest confidence level considering more elaborate planning algorithms.

Human-agent interaction for learning with instruction can would involve pointing the tank in at the enemy tank be viewed on a continuum of instructor/agent control. The environment is partially observable to the instructor or imitation. The other extreme of the continuum is and the task is unknown to the agent, necessitating mixed occupied by systems where instructor interaction is limited initiative, bidirectional information transfer. Our agents are instantiated in Soar (Laird, 2008), a To be able to maintain the state of interactions with the symbolic, cognitive architecture based on the problemspace instructor while acting in the environment, and to be able to hypothesis. A Soar agent's current state is derived learn from these instructions in the context they were from its perceptions, its beliefs about the world and provided in, the agent needs a model of task-oriented knowledge in its long-term memories and is held in its interaction.