George Ferguson

AAAI Conference 2013 Conference Paper

Crowd Formalization of Action Conditions

• Walter Lasecki
• Leon Weingard
• Jefffrey Bigham
• George Ferguson

Training intelligent systems is a time consuming and costly process that often limits their application to realworld problems. Prior work in crowdsourcing has attempted to compensate for this challenge by generating sets of labeled training data for machine learning algorithms. In this work, we seek to move beyond collecting just statistical data and explore how to gather structured, relational representations of a scenario using the crowd. We focus on activity recognition because of its broad applicability, high level of variation between individual instances, and difficulty of training systems a priori. We present ARchitect, a system that uses the crowd to ascertain pre and post conditions for actions observed in a video and find relations between actions. Our ultimate goal is to identify multiple valid execution paths from a single set of observations, which suggests one-off learning from the crowd is possible.

AAAI Conference 2012 Conference Paper

Real-Time Collaborative Planning with the Crowd

• Walter Lasecki
• Jeffrey Bigham
• James Allen
• George Ferguson

Planning is vital to a wide range of domains, including robotics, military strategy, logistics, itinerary generation and more, that both humans and computers find difficult. Collaborative planning holds the promise of greatly improving performance on these tasks by leveraging the strengths of both humans and automated planners. However, this requires formalizing the problem domain and input, which must be done by hand, a priori, restricting its use in general real-world domains. We propose using a real-time crowd of workers to simultaneously solve the planning problem, formalize the domain, and train an automated system. As plans are developed, the system is able to learn the domain, and contribute larger segments of work.

AAAI Conference 2007 Conference Paper

PLOW: A Collaborative Task Learning Agent

• James Allen
• George Ferguson
• Hyuckchul Jung

To be effective, an agent that collaborates with humans needs to be able to learn new tasks from humans they work with. This paper describes a system that learns executable task models from a single collaborative learning session consisting of demonstration, explanation and dialogue. To accomplish this, the system integrates a range of AI technologies: deep natural language understanding, knowledge representation and reasoning, dialogue systems, planning/agent-based systems and machine learning. A formal evaluation shows the approach has great promise.

AAMAS Conference 2002 Conference Paper

A Problem Solving Model for Collaborative Agents

• James Allen
• Nate Blaylock
• George Ferguson

AAAI Conference 1999 Conference Paper

TRIPS: The Rochester Interactive Planning System

• George Ferguson
• James F. Allen
• University of Rochester

This demonstration showcases TRIPS, The Rochester Interactive PlanningSystem, an intelligent, collaborative, conversational planning assistant. TRIPS collaborates with the user using both spoken dialogue and graphical displays to solve problemsin a transportation logistics domain. In our demonstrations, users are encouragedto sit downand try the system, with only rudimentary guidance from us. For further information, including QuickTime moviesof the systemin action, please visit our website at the URL listed above.

AAAI Conference 1998 Conference Paper

TRIPS: An Integrated Intelligent Problem-Solving Assistant

• George Ferguson

We discuss what constitutes an integrated system in AI, and why AI researchers should be interested in building and studying them. Taking integrated systems to be ones that integrate a variety of components in order to perform some task from start to finish, we believe that such systems (a) allow us to better ground our theoretical work in actual tasks, and (b) provide an opportunity for much-needed evaluation based on task performance. We describe one particular integrated system we have developed that supports spoken-language dialogue to collaboratively solve planning problems. We discuss how the integrated system provides key advantages for helping both our work in natural language dialogue processing and in interactive planning and problem solving, and consider the opportunities such an approach affords for the future. Content areas: AI systems, natural language understanding, planning and control, problem solving, user interfaces

ICAPS Conference 1996 Conference Paper

TRAINS-95: Towards a Mixed-Initiative Planning Assistant

• George Ferguson
• James F. Allen
• Bradford W. Miller

We have been examining mixed-initiative planning systems in the context of command and control or logistical overview situations. In such environments, the human and the computer must work together in a very tightly coupled way to solve problems that neither alone could manage. In this paper, we describe our implementation of a prototype version of such a system, TRAINS-95, which helps a manager solve routing problems in a simple transportation domain. Interestingly perhaps, traditional planning technology does not play a major role in the system, and in fact it is difficult to see how such components might fit into a mixed-initiative system. We describe some of these issues, and present our agenda for future research into mixed-initiative plan reasoning. At this writing, the TRAINS-95 system has been used by more than 100 people to solve simple problems at various conferences and workshops, and in our experiments.

ICAPS Conference 1994 Conference Paper

Arguing about Plans: Plan Representation and Reasoning for Mixed-initiative Planning

• George Ferguson
• James F. Allen

Weconsider the problem of representing plans for mixed-initiative planning, where several participants cooperate to develop plans. V~re claim that in such an environment, a crucial task is plan communication: the ability to suggest aspects of a plan, accept such suggestions from other agents, criticize plans, revise them, e~c., in addition to building plans. The complexity of this interaction imposessi$’nificant newrequirements on the representation of plans. Wedescribe a formal modelof plans based on defensible argumentsystems that allows us to perform these types of reasoning. The argumentsthat are producedare explicit objects that can be used to provide a semantics for statements about plans.