Pub Date : 2019-09-01DOI: 10.7551/mitpress/11956.003.0006
R. Wray, N. Taatgen, C. Lebiere, Katerina Pastra, P. Pirolli, P. Rosenbloom, matthias. scheutz, T. Stewart, Janet Wiles
What knowledge needs to be learned to acquire a novel task? What background knowledge does an agent need to use newly acquired knowledge effectively? This chapter considers the functional roles of knowledge in task learning. These roles of knowledge span interaction with other entities and the environment and core functional capabilities of the reasoning system itself (i.e., architecture). Perspectives are offered on the definition of "task" and the relationship between task and knowledge. In addition, three specific challenges central to the role of knowledge in interactive task learning (ITL) are examined: the identification of architectural primitives (basic functional and representational building blocks) needed for ITL, requirements for enabling shared understanding ("common ground") between learner and instructor, and conditions that support projection and anticipation of future states. In conclusion, specific research questions are put forth to address these challenges and advance ITL as a field of inquiry.
{"title":"Functional Knowledge Requirements for Interactive Task Learning","authors":"R. Wray, N. Taatgen, C. Lebiere, Katerina Pastra, P. Pirolli, P. Rosenbloom, matthias. scheutz, T. Stewart, Janet Wiles","doi":"10.7551/mitpress/11956.003.0006","DOIUrl":"https://doi.org/10.7551/mitpress/11956.003.0006","url":null,"abstract":"What knowledge needs to be learned to acquire a novel task? What background knowledge does an agent need to use newly acquired knowledge effectively? This chapter considers the functional roles of knowledge in task learning. These roles of knowledge span interaction with other entities and the environment and core functional capabilities of the reasoning system itself (i.e., architecture). Perspectives are offered on the definition of \"task\" and the relationship between task and knowledge. In addition, three specific challenges central to the role of knowledge in interactive task learning (ITL) are examined: the identification of architectural primitives (basic functional and representational building blocks) needed for ITL, requirements for enabling shared understanding (\"common ground\") between learner and instructor, and conditions that support projection and anticipation of future states. In conclusion, specific research questions are put forth to address these challenges and advance ITL as a field of inquiry.","PeriodicalId":270359,"journal":{"name":"Interactive Task Learning","volume":"31 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113931670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.7551/mitpress/11956.003.0011
A. Thomaz, E. Lieven, M. Cakmak, J. Chai, S. Garrod, Wayne D. Gray, S. Levinson, Ana Paiva, Nele Russwinkel
This chapter considers the qualities of human interaction and learning that will be most effective and natural to incorporate into any interactive task learning agent, and focuses specifi cally on the interactions involved in learning from explicit instruction. At the center of this interaction is a process that brings the common ground between a teacher agent and a learner agent into alignment. Errors or misalignments to this common ground drive the interactive learning process. The importance of timing is highlighted as is the dynamics of an interaction, as a communication channel itself, in this
{"title":"Interaction for Task Instruction and Learning","authors":"A. Thomaz, E. Lieven, M. Cakmak, J. Chai, S. Garrod, Wayne D. Gray, S. Levinson, Ana Paiva, Nele Russwinkel","doi":"10.7551/mitpress/11956.003.0011","DOIUrl":"https://doi.org/10.7551/mitpress/11956.003.0011","url":null,"abstract":"This chapter considers the qualities of human interaction and learning that will be most effective and natural to incorporate into any interactive task learning agent, and focuses specifi cally on the interactions involved in learning from explicit instruction. At the center of this interaction is a process that brings the common ground between a teacher agent and a learner agent into alignment. Errors or misalignments to this common ground drive the interactive learning process. The importance of timing is highlighted as is the dynamics of an interaction, as a communication channel itself, in this","PeriodicalId":270359,"journal":{"name":"Interactive Task Learning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131566511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-05-22DOI: 10.7551/mitpress/11956.003.0012
S. Levinson
{"title":"Natural Forms of Purposeful Interaction among Humans: What Makes Interaction Effective?","authors":"S. Levinson","doi":"10.7551/mitpress/11956.003.0012","DOIUrl":"https://doi.org/10.7551/mitpress/11956.003.0012","url":null,"abstract":"","PeriodicalId":270359,"journal":{"name":"Interactive Task Learning","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121829440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.7551/mitpress/11956.003.0007
{"title":"What People Learn from Instruction","authors":"","doi":"10.7551/mitpress/11956.003.0007","DOIUrl":"https://doi.org/10.7551/mitpress/11956.003.0007","url":null,"abstract":"","PeriodicalId":270359,"journal":{"name":"Interactive Task Learning","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122021982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.7551/mitpress/11956.003.0009
N. Taatgen
{"title":"The Representation of Task Knowledge at Multiple Levels of Abstraction","authors":"N. Taatgen","doi":"10.7551/mitpress/11956.003.0009","DOIUrl":"https://doi.org/10.7551/mitpress/11956.003.0009","url":null,"abstract":"","PeriodicalId":270359,"journal":{"name":"Interactive Task Learning","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116041810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.7551/mitpress/11956.003.0014
Wayne D. Gray, John K. Lindstedt, C. Sibert, Matthew-Donald D. Sangster, Roussel Rahman, Ropafadzo Denga, Marc Destefano
Studying the essence of interaction requires task environments in which changes may arise due to the nature of the environment or the actions of agents in that environment. In dynamic environments, the agent’s choice to do nothing does not stop the task environment from changing. Likewise, making a decision in such environments does not mean that the best decision, based on current information, will remain “best” as the task environment changes. This chapter summarizes work in progress which brings the tools of experimental psychology, machine learning, and advanced statistical analyses to bear on understanding the complexity of interactive performance in complex tasks involving single or multiple interactive agents in dynamic environments.
{"title":"The Essence of Interaction in Boundedly Complex, Dynamic Task Environments","authors":"Wayne D. Gray, John K. Lindstedt, C. Sibert, Matthew-Donald D. Sangster, Roussel Rahman, Ropafadzo Denga, Marc Destefano","doi":"10.7551/mitpress/11956.003.0014","DOIUrl":"https://doi.org/10.7551/mitpress/11956.003.0014","url":null,"abstract":"Studying the essence of interaction requires task environments in which changes may arise due to the nature of the environment or the actions of agents in that environment. In dynamic environments, the agent’s choice to do nothing does not stop the task environment from changing. Likewise, making a decision in such environments does not mean that the best decision, based on current information, will remain “best” as the task environment changes. This chapter summarizes work in progress which brings the tools of experimental psychology, machine learning, and advanced statistical analyses to bear on understanding the complexity of interactive performance in complex tasks involving single or multiple interactive agents in dynamic environments.","PeriodicalId":270359,"journal":{"name":"Interactive Task Learning","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124475452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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