New views of Colonial MexicoHaciendas and Ranchos in the Mexican Bajió: León 1700-1860, by D. A. Brading. Cambridge University Press, London, 1978. Tables, figures, maps, index, bibliography. 255 pp.Drinking, Homicide and Rebellion in Colonial Mexican Villages, by William B. Taylor. Stanford University Press, Stanford, 1979. Maps, tables, appendices, index, bibliography. 242 pp.Race and Class in Colonial Oaxaca, by John B. Chance. Stanford University Press, Stanford, 1978. Maps, tables, appendices, bibliography, index. 250 pp.

1980 ◽  
Vol 15 (1) ◽  
pp. 83-90
Author(s):  
Richard Boyer
Keyword(s):  
Stanford University ◽  
Cambridge University

scholarly journals Interactive task learning via embodied corrective feedback

2020 ◽  
Vol 34 (2) ◽  
Author(s):  
Mattias Appelgren ◽  
Alex Lascarides
Keyword(s):  
Learning Process ◽  
Corrective Feedback ◽  
Domain Model ◽  
Stanford University ◽  
Cambridge University ◽  
Task Learning ◽  
Discourse Coherence ◽  
Linguistic Evidence ◽  
Interactive Task

Abstract This paper addresses a task in Interactive Task Learning (Laird et al. IEEE Intell Syst 32:6–21, 2017). The agent must learn to build towers which are constrained by rules, and whenever the agent performs an action which violates a rule the teacher provides verbal corrective feedback: e.g. “No, red blocks should be on blue blocks”. The agent must learn to build rule compliant towers from these corrections and the context in which they were given. The agent is not only ignorant of the rules at the start of the learning process, but it also has a deficient domain model, which lacks the concepts in which the rules are expressed. Therefore an agent that takes advantage of the linguistic evidence must learn the denotations of neologisms and adapt its conceptualisation of the planning domain to incorporate those denotations. We show that by incorporating constraints on interpretation that are imposed by discourse coherence into the models for learning (Hobbs in On the coherence and structure of discourse, Stanford University, Stanford, 1985; Asher et al. in Logics of conversation, Cambridge University Press, Cambridge, 2003), an agent which utilizes linguistic evidence outperforms a strong baseline which does not.

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