E. Charniak and R. Goldman. A Bayesian model of plan recognition. In Proceedings of the National Conference on Artificial Intelligence (AAAI), 1993. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
First 50 documents Next 50
Probabilistic Plan Recognition for Cognitive Apprenticeship - Cristina Conati And (1996) (1 citation) (Correct)
....has been following so that it can construct an appropriate hint. The problem of inferring from an agent s actions the plan or line of reasoning being followed is known in AI as plan recognition [Kautz and Allen, 1986] Plan recognition usually involves inherent uncertainty [Carberry, 1990, Charniak and Goldman, 1993, Huber et al. 1994] and in cognitive apprenticeship it is an especially hard problem [Self, 1988] since cognitive apprenticeships teach intellectual skills where most of the important activity is hidden from the coaches view. In this paper we describe an evolving student modeling framework, ....
Charniak, E. and Goldman, R. (1993). A bayesian model of plan recognition. Artificial Intelligence, 64(1):53--79.
Incremental Case-Based Plan Recognition with Local Predictions - Kerkez, Cox (Correct)
....plans and selecting the most appropriate plan among the competing plan hypothesis are two of the most important and most widely investigated components of both the plan recognition process and the case based process. Scientific methods used in this critical phase include Bayesian reasoning [16], 48] dynamic belief networks [3] decision theoretic approaches [50] rationality of coherency [4] Dempster Shafer theory [8] abduction [6] and case based reasoning [13] just to name a few. However, all of the above mentioned systems utilize complete plan libraries constructed a priori. ....
Charniak, E., & Goldman, R. (1993), "A Bayesian Model of Plan Recognition", Artificial Intelligence 64:53-79.
Identity Uncertainty and Citation Matching - Pasula, Marthi, Milch, Russell, .. (2003) (16 citations) (Correct)
....models (e.g. Gaussian noise) and assume that observations at each time step are all distinct. More general patterns of identity occur in natural language text, where the problem of anaphora resolution involves determining whether phrases (especially pronouns) co refer; some recent work [8] has used an early form of relational probability model, although with a somewhat counterintuitive semantics. Citeseer is the best known example of work on citation matching [1] The system groups citations using a form of greedy agglomerative clustering based on a text similarity metric (see ....
.... groups citations using a form of greedy agglomerative clustering based on a text similarity metric (see Section 5) McCallum et al. [9] use a similar technique, but also develop clustering algorithms designed to work well with large numbers of small clusters (see Section 4) With the exception of [8], all of the preceding systems have used domain specific algorithms and data structures; the probabilistic approaches are based on a fixed probability model. In previous work [10] we have suggested a declarative approach to identity uncertainty using a formal language an extension of relational ....
E. Charniak and R. P. Goldman. A Bayesian model of plan recognition. AAAI, 1993.
Retroactive Recognition of Interleaved Plans for Natural.. - Blaylock (2001) (3 citations) (Correct)
....[AZN98] dynamic belief networks were trained to predict a user s goal based on observed actions in a multi user dungeon video game. Horvitz and Paek [HP99, PH00, HP00, PHR00] use dynamic Bayesian Networks to recognize user intentions in several dialogue domains. Charniak and Goldman [Gol90, CG91, CG93] built an entire natural language understanding system, including plan recognition, in a unified dynamic belief network. Plan hypotheses were generated by piecing together evidence (previous utterances, plan roles of items in the current utterance, etc. A priori probabilities of the likelihood ....
....of the slots, and then ranking the best by a set of heuristics. It does not seem that this approach would generalize to plan recognition. There is hope of a possible solution, which may be able to be built on the belief network plan recognition system of Goldman and Charniak [Gol90, CG91, CG93] described in Section 5.3 above) Although the focus of their research does not appear to be incremental understanding, we believe that there are concepts in this work that could be used for incremental plan recognition. Firstly, their system seems to already support a limited form of ....
Eugene Charniak and Robert P. Goldman. A Bayesian model of plan recognition. Artificial Intelligence, 64(1):53--79, 1993. 37
Attention During Argument Generation And Presentation - Zukerman, McConachy, Korb (1998) (2 citations) (Correct)
....Argument I Argument Proposition Strategist I Xtt[o n a[ USER User Argument Inquiry Goal Proposition Figure 1: System Architecture goal, avoiding some distractions. During argument presentation, the Attentional Mechanism supports the generation of enthymematic arguments. 2 Related Research charniak and Goldman (1993) describe a Bayesian plan recognition system that uses marker passing as a method for focusing attention on a manageable portion of the space of all possible plans. This is analogous to the way in which NAG uses spreading activation to focus on a small portion of the available data during the ....
Charniak, E. and Goldman, R. P. (1993). A Bayesian model of plan recognition. Artificial Intelligence, 64(1):50-56.
Recognising and Monitoring High-Level Behaviours in.. - Nguyen, Bui.. (2003) (4 citations) (Correct)
....approaches employ a flat model of activities. To develop scalable systems for high level behaviour recognition, we need a framework that utilizes the inherent hierarchical structure. Recognizing high level, semantically rich behaviours has traditionally been the focus of plan recognition work [11, 6]. Sophisticated stochastic models for representing high level behaviours have been used such as Dynamic Bayesian Networks (DBN) 2] Abstract Hidden Markov Models (AHMM) 5] stochastic grammars (including Stochastic Context Free Grammar (SCFG) 17] and its state dependent extension Probabilistic ....
E. Charniak and R. P. Goldman. A Bayesian model of plan recognition. Artificial Intelligence, pages 53--79, 1993.
Case-Based Plan Recognition in Computer Games - Fagan, Cunningham (2003) (Correct)
....Plan Recognition (PR) is the process whereby an agent observes the actions of another agent with the objective of inferring the agent s future actions, intentions or goals. Several methods for plan recognition have been explored. The most notable are deductive [1] abductive [2] probabilistic [3] and case based [4] PR approaches may also be classified according to whether the PR process was intended [1] or keyhole [5] If the observed agent cooperates to convey his or her intentions to the recognising agent, as in natural language dialogue systems [6] then the PR process is said to be ....
Charniak, E. and Goldman, R., A Bayesian Model of Plan Recognition, Artificial Intelligence Journal, Vol. 64, pp. 53-79, 1993.
A General Model for Online Probabilistic Plan Recognition - Bui (2003) (5 citations) (Correct)
....and in the agent s observations of the plan. Dealing with these issues in plan recognition is a challenging task, especially when the recognition has to be done online so that the observer can react to the actor s plan in real time. The uncertainty problem has been addressed by the seminal work [Charniak and Goldman, 1993] which phrases the plan recognition problem as the inference problem in a Bayesian network representing the process of executing the actor s plan. More recent work has considered dynamic models for performing plan recognition online [Pynadath and Wellman, 1995; 2000; Goldmand et al. 1999; Huber ....
E. Charniak and R. P. Goldman. A Bayesian model of plan recognition. Artificial Intelligence, 64:53--79, 1993.
Corpus-based, Statistical Goal Recognition - Blaylock, Allen (2003) (Correct)
....recognizer depends on the existence of a plan corpus. If a plan corpus exists or can be created for the new domain (see section below) all we have to do is use it to train models for the new domain. On the other hand, most goal recognizers (e.g. Vilain, 1990; Carberry, 1990a; Kautz, 1991; Charniak and Goldman, 1993; Paek and Horvitz, 2000] require a complete, hand crafted plan library in order to perform recognition, which can require a significant amount of knowledge engineering for each domain. Granted, these systems are performing plan recognition and not just goal recognition, which makes the ....
....predictions, as they are unable to distinguish between consistent goals, even if one is more likely than the other. There are several lines of research which incorporate probabilistic reasoning into plan and goal recognition. Carberry, 1990a] and [Bauer, 1994] use Dempster Shafer theory and [Charniak and Goldman, 1993] , Pynadath and Wellman, 1995] and [Paek and Horvitz, 2000] use Belief Networks to represent the likelihood of possible plans and goals to be attributed to the user. All of these methods, however, require a complete plan library as well as the assignment of probability distributions over the ....
Eugene Charniak and Robert P. Goldman. A Bayesian model of plan recognition. Artificial Intelligence, 64(1):53--79, 1993.
Efficient Approximate Inference for Online Probabilistic Plan.. - Bui (2002) (Correct)
....and in the agent s observations of the plan. Dealing with these issues in plan recognition is a challenging task, especially when the recognition has to be done online so that the observer can react to the actor s plan in real time. The uncertainty problem has been addressed by the seminal work [7] which phrases the plan recognition problem as the inference problem in a Bayesian network representing the process of executing the actor s plan. More recent work has considered dynamic models for performing plan recognition online [20, 21, 11, 12, 1] While this offers a coherent way of ....
E. Charniak and R. P. Goldman. A Bayesian model of plan recognition. Artificial Intelligence, 64:53--79, 1993.
Retroactive Recognition of Interleaved Plans for Natural.. - Blaylock (2001) (3 citations) (Correct)
....[AZN98] dynamic belief networks were trained to predict a user s goal based on observed actions in a multi user dungeon video game. Horvitz and Paek [HP99, PH00, HP00, PHR00] use dynamic Bayesian Networks to recognize user intentions in several dialogue domains. Charniak and Goldman [Gol90, CG91, CG93] built an entire natural language understanding system, including plan recognition, in a uni ed dynamic belief network. Plan hypotheses were generated by piecing together evidence (previous utterances, plan roles of items in the current utterance, etc. A priori probabilities of the likelihood ....
....of the slots, and then ranking the best by a set of heuristics. It does not seem that this approach would generalize to plan recognition. There is hope of a possible solution, which may be able to be built on the belief network plan recognition system of Goldman and Charniak [Gol90, CG91, CG93] described in Section 5.3 above) Although the focus of their research does not appear to be incremental understanding, we believe that there are concepts in this work that could be used for incremental plan recognition. Firstly, their system seems to already support a limited form of ....
Eugene Charniak and Robert P. Goldman. A Bayesian model of plan recognition. Arti cial Intelligence, 64(1):53-79, 1993. 37
A Cost Minimization Approach to Human Behavior Recognition - Sukthankar, Sycara (2005) (Correct)
No context found.
E. Charniak and R. Goldman. A Bayesian model of plan recognition. In Proceedings of the National Conference on Artificial Intelligence (AAAI), 1993.
Automatic Recognition of Human Team Behaviors - Sukthankar, Sycara (2005) (Correct)
No context found.
E. Charniak and R. Goldman. A Bayesian model of plan recognition. In Proceedings of the National Conference on Artificial Intelligence (AAAI), 1993.
A Utility-Based Approach to Intention Recognition - Mao, Gratch (2004) (Correct)
No context found.
E. Charniak and R. Goldman. A Bayesian Model of Plan Recognition. Artificial Intelligence, 64(1):53-79, 1993.
Fast and Complete Symbolic Plan Recognition: - Allowing For Duration (Correct)
No context found.
Eugene Charniak and Robert P. Goldman. A Bayesian model of plan recognition. AIJ, 64(1):53--79, November 1993.
Recognizing Instantiated Goals Using Statistical Methods - Blaylock, Allen (Correct)
No context found.
Eugene Charniak and Robert P. Goldman. A Bayesian model of plan recognition. Artificial Intelligence, 64(1):53--79, 1993.
Corpus-based, Statistical Goal Recognition - Nate Blaylock And (Correct)
No context found.
Eugene Charniak and Robert P. Goldman. A Bayesian model of plan recognition. Artificial Intelligence, 64(1):53--79, 1993.
A Planner for Plan Construction Dialogues for Multi-Agent Plans - McEleney, O'Hare (2004) (Correct)
No context found.
Charniak, E., Goldman, R. : A Bayesian Model of Plan Recognition. Artificial Intelligence 64 (1) (1993) 53-79
Generating Artificial Corpora for Plan Recognition - Blaylock, Allen (2005) (Correct)
No context found.
Charniak, E., Goldman, R.P.: A Bayesian model of plan recognition. Artificial Intelligence 64 (1993) 53--79
Dynamic Choice of Robust Strategies in Dialogue Management - Bryan Mceleney And (Correct)
No context found.
Charniak, E., Goldman, R., A Bayesian Model of Plan Recognition. Artificial Intelligence 64 (1) 53-79, 1993.
A Planner for Plan Construction Dialogues for Multi-Agent Plans - McEleney, O'Hare (Correct)
No context found.
Charniak, E., Goldman, R. : A Bayesian Model of Plan Recognition. Artificial Intelligence 64 (1) (1993) 53-79
Identity Uncertainty and Citation Matching - Pasula, Marthi, Milch, Russell, .. (2002) (16 citations) (Correct)
No context found.
E. Charniak and R. P. Goldman. A Bayesian model of plan recognition. AAAI, 1993.
Programming by Demonstration: a Machine Learning Approach - Lau (2001) (1 citation) (Correct)
No context found.
E. Charniak and R.P. Goldman. A Bayesian model of plan recognition. Arti cial Intelligence, 64(1):53-79, 1993.
Corpus-based, Statistical Goal Recognition - Nate Blaylock And (Correct)
No context found.
Eugene Charniak and Robert P. Goldman. A Bayesian model of plan recognition. Artificial Intelligence, 64(1):53--79, 1993.
Managing Communicative Intentions in Dialogue Using a.. - Blaylock (2002) (Correct)
No context found.
Eugene Charniak and Robert P. Goldman, \A Bayesian Model of Plan Recognition," Arti cial Intelligence, 64(1):53-79, 1993.
First 50 documents Next 50
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC