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79
Adopt: asynchronous distributed constraint optimization with quality guarantees
- ARTIFICIAL INTELLIGENCE LABORATORY, MASSACHUSETTS INSTITUTE OF TECHNOLOGY
, 2005
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Towards Adjustable Autonomy for the Real World
, 2003
"... Adjustable autonomy refers to entities dynamically varying their own autonomy, transferring decision-making control to other entities (typically agents transferring control to human users) in key situations. Determining whether and when such transfers-of-control should occur is arguably the funda ..."
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Cited by 121 (42 self)
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Adjustable autonomy refers to entities dynamically varying their own autonomy, transferring decision-making control to other entities (typically agents transferring control to human users) in key situations. Determining whether and when such transfers-of-control should occur is arguably the fundamental research problem in adjustable autonomy. Previous work has investigated various approaches to addressing this problem but has often focused on individual agent-human interactions. Unfortunately, domains requiring collaboration between teams of agents and humans reveal twokey shortcomings of these previous approaches. First, these approaches use rigid one-shot transfers of control that can result in unacceptable coordination failures in multiagent settings. Second, they ignore costs (e.g., in terms of time delays or eects on actions) to an agent's team due to such transfers-ofcontrol.
Wrapper Maintenance: A Machine Learning Approach
- Journal of Artificial Intelligence Research
, 2003
"... The proliferation of online information sources has led to an increased use of wrappers for extracting data from Web sources. While most of the previous research has focused on quick and e#cient generation of wrappers, the development of tools for wrapper maintenance has received less attention. ..."
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Cited by 86 (17 self)
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The proliferation of online information sources has led to an increased use of wrappers for extracting data from Web sources. While most of the previous research has focused on quick and e#cient generation of wrappers, the development of tools for wrapper maintenance has received less attention. This is an important research problem because Web sources often change in ways that prevent the wrappers from extracting data correctly. We present an e#cient algorithm that learns structural information about data from positive examples alone. We describe how this information can be used for two wrapper maintenance applications: wrapper verification and reinduction. The wrapper verification system detects when a wrapper is not extracting correct data, usually because the Web source has changed its format. The reinduction algorithm automatically recovers from changes in the Web source by identifying data on Web pages so that a new wrapper may be generated for this source. To validate our approach, we monitored 27 wrappers over a period of a year.
CMRadar: A Personal Assistant Agent for Calendar Management
- In Agent Oriented Information Systems, (AOIS
, 2004
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An intelligent personal assistant for task and time management
- AI MAGAZINE 28(2):47–61
, 2007
"... We describe an intelligent personal assistant that has been developed to aid a busy knowledge worker in managing time commitments and performing tasks. The design of the system was motivated by the complementary objectives of (1) relieving the user of routine tasks, thus allowing her to focus on tas ..."
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Cited by 48 (17 self)
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We describe an intelligent personal assistant that has been developed to aid a busy knowledge worker in managing time commitments and performing tasks. The design of the system was motivated by the complementary objectives of (1) relieving the user of routine tasks, thus allowing her to focus on tasks that critically require human problem-solving skills, and (2) intervening in situations where cognitive overload leads to oversights or mistakes by the user. The system draws on a diverse set of AI technologies that are linked within a Belief-Desire-Intention (BDI) agent system. Although the system provides a number of automated functions, the overall framework is highly user centric in
Ad hoc autonomous agent teams: Collaboration without pre-coordination
- In Proc. of the 24th AAAI Conf. on Artificial Intelligence
, 2010
"... As autonomous agents proliferate in the real world, both in software and robotic settings, they will increasingly need to band together for cooperative activities with previously unfamiliar teammates. In such ad hoc team settings, team strategies cannot be developed a priori. Rather, an agent must b ..."
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Cited by 48 (20 self)
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As autonomous agents proliferate in the real world, both in software and robotic settings, they will increasingly need to band together for cooperative activities with previously unfamiliar teammates. In such ad hoc team settings, team strategies cannot be developed a priori. Rather, an agent must be prepared to cooperate with many types of teammates: it must collaborate without pre-coordination. This paper challenges the AI community to develop theory and to implement prototypes of ad hoc team agents. It defines the concept of ad hoc team agents, specifies an evaluation paradigm, and provides examples of possible theoretical and empirical approaches to challenge. The goal is to encourage progress towards this ambitious, newly realistic, and increasingly important research goal. 1
Getting from Here to There: Interactive Planning and Agent Execution for Optimizing Travel
- In IAAI
, 2002
"... Planning and monitoring a trip is a common but complicated human activity. Creating an itinerary is nontrivial because it requires coordination with existing schedules and making a variety of interdependent choices. Once planned, there are many possible events that can affect the plan, such as sched ..."
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Cited by 39 (11 self)
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Planning and monitoring a trip is a common but complicated human activity. Creating an itinerary is nontrivial because it requires coordination with existing schedules and making a variety of interdependent choices. Once planned, there are many possible events that can affect the plan, such as schedule changes or flight cancellations, and checking for these possible events requires time and effort. In this paper, we describe how Heracles and Theseus, two information gathering and monitoring tools that we built, can be used to simplify this process. Heracles is a hierarchical constraint planner that aids in interactive itinerary development by showing how a particular choice (e.g., destination airport) affects other choices (e.g., possible modes of transportation, available airlines, etc.). Heracles builds on an information agent platform, called Theseus, that provides the technology for efficiently executing agents for information gathering and monitoring tasks. In this paper we present the technologies underlying these systems and describe how they are applied to build a state-of-the-art travel system.
Human directability of agents
- in Proc. K-CAP
, 2001
"... Many potential applications for agent technology require hu-mans and agents to work together in order to achieve com-plex tasks effectively. In contrast, much of the work in the agents community to date has focused on technologies for fully autonomous agent systems. This paper presents a frame-work ..."
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Cited by 38 (5 self)
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Many potential applications for agent technology require hu-mans and agents to work together in order to achieve com-plex tasks effectively. In contrast, much of the work in the agents community to date has focused on technologies for fully autonomous agent systems. This paper presents a frame-work for the directability of agents, in which a human su-pervisor can define policies to influence agent activities at execution time. The framework focuses on the concepts of adjustable autonomy for agents (i.e., varying the degree to which agents make decisions without human intervention) and strategy preference (i.e., recommending how agents should accomplish assigned task). The directability framework has been implemented within a PRS environment, and applied to a multiagent intelligence-gathering domain.
A General Methodology for Mathematical Analysis of Multi-Agent Systems
- USC Information Sciences
, 2001
"... We propose a general mathematical methodology for studying the dynamics of multiagent systems in which complex collective behavior arises out of local interactions between many simple agents. The mathematical model is composed of a system of coupled differential equations describing the macroscop ..."
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Cited by 34 (4 self)
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We propose a general mathematical methodology for studying the dynamics of multiagent systems in which complex collective behavior arises out of local interactions between many simple agents. The mathematical model is composed of a system of coupled differential equations describing the macroscopic, or collective, dynamics of an agent-based system. We illustrate our approach by applying it to analyze several agent-based systems, including coalition formation in an electronic marketplace, and foraging and collaboration in a group of robots. 1.
Intelligent control of a water recovery system: Three years
- AI Magazine
, 2003
"... • This article discusses our experience building and running an intelligent control system during a two-year test for a NASA advanced life support (ALS) system. The system under test was known as the integrated water recovery system (iWRS). We used the 3T intelligent control architecture to produce ..."
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Cited by 31 (13 self)
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• This article discusses our experience building and running an intelligent control system during a two-year test for a NASA advanced life support (ALS) system. The system under test was known as the integrated water recovery system (iWRS). We used the 3T intelligent control architecture to produce software that operated autonomously, 24/7 for sixteen months. The article details our development approach, the successes and failures of the system and our lessons learned. We conclude with a summary of spin-off benefits to the AI community and areas of AI research that can be useful for future ALS systems. "We'll have to go with four two-head pumps for the nitrifier." The AI controls engineer frowned at the speaker, a young mechanical engineer in charge of the physical design of a state-of-the-art biological water processor (BWP). "But that pump doesn't give me any feedback for speed, so we can't be sure it's responding to commands." "It'll have to do, " said a woman at the far end of the conference table. As the manager for the integrated water recovery system (iWRS), she made the final calls. "The eight-head pump won't function at the required pressures and the four-heads are just too expensive. Can't you use the tube pressures to know if the pumps are working?"
