An algorithm for plan recognition in collaborative discourse

Lochbaum, Karen E.

doi:10.3115/981344.981349

Cited by 25 publications

(22 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Here, one agent plans its utterances or speech acts intending for them to be interpreted and understood in specific ways. Seminal work in this area was carried out by Sidner [43] and later Lochbaum [44], who have focused on collaborative dialogue settings. However, unlike our work, their focus is on the interpretation (the recognition), rather than on the planning of observed actions.…”

Section: Repeated Scenarios With Simultaneous Actionsmentioning

confidence: 99%

Teaching and leading an ad hoc teammate: Collaboration without pre-coordination

Stone

Kaminka

Kraus

et al. 2013

Artificial Intelligence

View full text Add to dashboard Cite

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 article defines two aspects of collaboration in two-player teams, involving either simultaneous or sequential decision making. In both cases, the ad hoc agent is more knowledgeable of the environment, and attempts to influence the behavior of its teammate such that they will attain the optimal possible joint utility.

show abstract

Section: Repeated Scenarios With Simultaneous Actionsmentioning

confidence: 99%

Teaching and leading an ad hoc teammate: Collaboration without pre-coordination

Stone

Kaminka

Kraus

et al. 2013

Artificial Intelligence

View full text Add to dashboard Cite

show abstract

“…Here, one agent plans its utterances or speech acts intending for them to be interpreted and understood in specific ways. Seminal work in this area was carried out by Sidner [14] and later Lochbaum [10], who have focused on collaborative dialogue settings. However, unlike our work, their focus is on the interpretation (the recognition), rather than on the planning of observed actions.…”

Section: Related Workmentioning

confidence: 99%

Leading a Best-Response Teammate in an Ad Hoc Team

Stone

Kaminka

Rosenschein

2010

Lecture Notes in Business Information Processing

View full text Add to dashboard Cite

Abstract. Teams of agents may not always be developed in a planned, coordinated fashion. Rather, as deployed agents become more common in e-commerce and other settings, there are increasing opportunities for previously unacquainted agents to cooperate in ad hoc team settings. In such scenarios, it is useful for individual agents to be able to collaborate with a wide variety of possible teammates under the philosophy that not all agents are fully rational. This paper considers an agent that is to interact repeatedly with a teammate that will adapt to this interaction in a particular suboptimal, but natural way. We formalize this setting in game-theoretic terms, provide and analyze a fully-implemented algorithm for finding optimal action sequences, prove some theoretical results pertaining to the lengths of these action sequences, and provide empirical results pertaining to the prevalence of our problem of interest in random interaction settings.

show abstract

“…In contrast to the multi-level approach to dialogue processing, Grosz, Sidner and Lochbaum only use domain-level plans to model collaborative dialogue (Grosz & Sidner 1986, Lochbaum et al 1990, Lochbaum 1991, Lochbaum 1995. We agree with Lochbaum that it is better not to introduce multiple plan types, but: -Although we have three plan libraries, only the object-level and the metalevel plan libraries have functionally distinct roles.…”

Section: The System Implementationmentioning

confidence: 99%

A Replanning Algorithm for Decision Theoretic Hierarchical Planning: Principles and Empirical Evaluation

Boella

Damiano

2008

Applied Artificial Intelligence

View full text Add to dashboard Cite

In this paper, we present a replanning algorithm for a decision-theoretic hierarchical planner, and illustrate the experimental methodology we designed to investigate its performance. The methodology relies on an agentbased framework, in which plan failures emerge from the interplay of the agent and the environment. Given this framework, the replanning algorithm is compared with planning from scratch by executing experiments in different domains. The empirical evaluation shows the superiority of replanning with respect from planning from scratch. However, the observation of significant differences in the data collected across planning domains confirm the importance of empirical evaluation in practical systems.

show abstract

An algorithm for plan recognition in collaborative discourse

Cited by 25 publications

References 8 publications

Teaching and leading an ad hoc teammate: Collaboration without pre-coordination

Teaching and leading an ad hoc teammate: Collaboration without pre-coordination

Leading a Best-Response Teammate in an Ad Hoc Team

A Replanning Algorithm for Decision Theoretic Hierarchical Planning: Principles and Empirical Evaluation

Contact Info

Product

Resources

About