This paper describes an implementation of the 3T robot architecture which has been under development for the last eightyears. The architecture uses three levels of abstraction and description languages whichare compatible between levels. The makeup of the architecture helps to coordinate planful activities with real-time behaviors for dealing with dynamic environments. In recent years, other architectures have been created with similar attributes but two features distinguish the 3T architecture: 1) a variety of useful software tools have been created to help implement this architecture on multiple real robots;, and 2) this architecture, or parts of it, have been implemented on a varietyofvery different robot systems using different processors, operating systems, effectors and sensor suites.

We are studying how to create believable agents that perform actions and use natural language in interactive, animated, real-time worlds. Believable agents are autonomous agents that have specific, rich personalities like characters in movies and animation. We have extended Hap, the behavior-based architecture used by the Oz group to construct non-linguistic believable agents, to support natural language text generation. These extensions allow us to tightly integrate text generation with other aspects of the agent, including action, perception, inference and emotion. We describe our approach, and show how it leads to agents with properties we believe important for believability, such as: using language and action together to accomplish communication goals; using perception to help make linguistic choices; varying generated text according to emotional state; varying generated text to express the specific personality; and issuing the text in real-time with pauses, restarts and other brea...

This paper presents an approach to learning from situated, interactive tutorial instruction within an ongoing agent. Tutorial instruction is a flexible (and thus powerful) paradigm for teaching tasks because it allows an instructor to communicate whatever types of knowledge an agent might need in whatever situations might arise. To support this flexibility, however, the agent must be able to learn multiple kinds of knowledge from a broad range of instructional interactions. Our approach, called situated explanation, achieves such learning through a combination of analytic and inductive techniques. It combines a form of explanation-based learning that is situated for each instruction with a full suite of contextually guided responses to incomplete explanations. The approach is implemented in an agent called Instructo-Soar that learns hierarchies of new tasks and other domain knowledge from interactive natural language instructions. Instructo-Soar meets three key requirements of flexible...

In contrast to current intelligent systems, which must be laboriously programmed for each task they are meant to perform, instructable agents can be taught new tasks and associated knowledge. This thesis presents a general theory of learning from tutorial instruction and its use to produce an instructable agent. Tutorial instruction is a particularly powerful form of instruction, because it allows the instructor to communicate whatever kind of knowledge a student needs at whatever point it is needed. To exploit this broad flexibility, however, a tutorable agent must support a full range of interaction with its instructor to learn a full range of knowledge. Thus, unlike most machine learning tasks, which target deep learning of a single kind of knowledge from a single kind of input, tutorability requires a breadth of learning from a broad range of instructional interactions. The theory of learning from tutorial...

In this paper we briefly describe a software architecture that integrates reactivity and deliberation. The architecture has three levels, or tiers. The bottom tier contains a set of situated, reactive skills. The second tier is a sequencer that can activate sets of skills to perform a task. The third tier is a planner that reasons in-depth about goals, resources and constraints. We characterize this architecture across several dimensions and then describe the lessons that we have learned from applying this architecture to several robotics projects. Introduction For several years we have investigated ways to combine deliberation and reactivity in robot control architectures to program robots to carry out tasks robustly in field environments (Bonasso 1991; Bonasso, Antonisse, & Slack 1992). We believe this integration is crucial. Not only must an agent be able to adjust to changes in a dynamic situation, but it must be able to synthesize plans, since the complexities of the real world m...

We are studying how to create believable agents that perform actions and use natural language in interactive, animated, real-time worlds. We have extended Hap, our behavior-based architecture for believable non-linguistic agents, to better support natural language text generation. These extensions allow us to tightly integrate generation with other aspects of the agent, including action, perception, inference and emotion. We describe our approach, and show how it leads to agents with properties we believe important for believability, such as: using language and action together to accomplish communication goals; using perception to help make linguistic choices; varying generated text according to emotional state; and issuing the text in real-time with pauses, restarts and other breakdowns visible. Besides being useful in constructing believable agents, we feel these extensions may interest researchers seeking to generate language in other action architectures.

This paper describes an implementation of the 3 T robot architecture which has been under development for the last eightyears. The architecture uses three levels of abstraction and description languages whichare compatible between levels. The makeup of the architecture helps to coordinate planful activities with real-time behaviors for dealing with dynamic environments. In recentyears, other architectures have been created with similar attributes but two features distinguish the 3 Tarchitecture: 1) a variety of useful software tools have been created to help implement this architecture on multiple real robots# and 2) this architecture, or parts of it, have been implemented on a varietyofvery different robot systems using different processors, operating systems, effectors and sensor suites.

Copyright © 2009 SAE International A Lunar habitat will be highly sensored and generate large amounts of data or telemetry. For this data to be useful to humans monitoring these systems and to automated algorithms controlling these systems it will need to be converted into more abstract data. This abstracted data will reflect the trends, states and characteristics of the systems and their environments. Currently this data abstraction process is manual and ad hoc. We are developing a Data Abstraction Architecture (DAA) that allows engineers to design software processes that iteratively convert habitat data into higher and higher levels of abstraction. The DAA is a series of mathematical or logical transformations of telemetry data to provide appropriate inputs from a hardware system to a hardware system controller, system engineer, or crew. The DAA also formalizes the relationships between data and control and the relationships between the data themselves. We have connected our Data Abstraction Architecture to a simulation of a Lunar habitat in order to test its ability to aid in the monitoring and control functions.