Ernst Strüngmann Forum


Interactive Task Learning

Agents, Robots, and Humans Acquiring
New Tasks through Natural Interaction

May 21–26, 2017

Frankfurt am Main, Germany

Kevin A. Gluck and John E. Laird, Chairpersons

Program Advisory Committee

Kenneth M. Ford, Institute for Human and Machine Cognition, Pensacola, FL, U.S.A.
Kevin A. Gluck, Air Force Research Laboratory, Wright-Patterson AFB, OH, U.S.A.
John E. Laird, University of Michigan, Ann Arbor, MI, U.S.A.
Elena Lieven, Manchester University, Manchester, M13 9PL, U.K.
Julia Lupp, Ernst Strüngmann Forum, 60438 Frankfurt, Germany
Luc Steels, Vrije Universiteit Brussels, B-1050 Brussels, Belgium
Niels Taatgen, University of Groningen, Department of Artificial Intelligence, Groningen, The Netherlands


Understanding the acquisition of new tasks through natural interaction is a fundamental unsolved problem. It is an inherently multidisciplinary challenge, which impedes progress due to the fractionated state of the relevant scientific and technical disciplines. This Forum will be a catalyzing event to achieve the following goals:

  • Define the problem of interactive task learning from diverse perspectives.

  • Identify the most important scientific gaps that must be addressed to understand interactive task learning in humans and achieve the vision for interactive task learning in artificially intelligent systems.

  • Identify a variety of possible scientific and technical approaches to closing those gaps, drawing from a multiplicity of scientific fields.

  • Relate the closing of those gaps to concrete new capabilities needed in assistive robotics, healthcare, education, training, and gaming.

  • Establish a diverse international community of scientists committed to the study of interactive task learning.

Insights gained from this Forum will provide a foundational reference and organizing framework for global research and development in interactive task learning.

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Background and Motivation for the Forum

Humans are not limited to a fixed set of innate or preprogrammed tasks. We quickly learn novel tasks through language and other forms of natural communication, and once we learn them, we learn to perform them better. We learn to play new games in just a few minutes; we learn how to use new devices such as smart phones, computers, and industrial machinery; and we can learn how to help a disabled family member with their everyday tasks, adapting to their needs over time. Advances in artificial intelligence, cognitive science, and robotics are leading us to future systems with impressive cognitive and physical capabilities. However, due to the dynamic, non-stationary environments in which such systems will need to operate, it is impossible to anticipate ahead of time and pre-program all of the knowledge required for these systems to meet their functional requirements. We want systems that are more like partners or teammates, and not merely tools.

How will these future systems learn the unanticipated and evolving complex tasks we want them to perform?

How can this endless variety of new requirements be learned quickly through natural interaction with people?

Currently, only isolated research is being conducted on this problem. Most of the related work ignores and avoids the reality that we need more basic research on the fundamental nature of interactive task learning. Our objective is to catalyze the global research community to pursue the science and technology necessary for interactive task learning; that is, how humans learn new tasks from each other, and how can we develop intelligent artificial agents that also learn and teach new tasks through natural interactions with humans. This is an extremely ambitious problem to tackle, but recent progress in many of the related fields suggests that now is the time to make a cooperative and coordinated push toward interactive task learning.

Understanding the acquisition of new tasks through natural interaction is a fundamental, unsolved problem. Pursuing it will increase our understanding of how both humans and artificial agents convert an externally communicated description or demonstration into efficient executable procedural knowledge that is incrementally and dynamically integrated with existing knowledge. This requires both the assimilation of new knowledge with existing knowledge, and the accommodation of existing knowledge to the new knowledge. Extending our understanding of the computational processes involved when humans learn new tasks will be a major advance for cognitive modeling and provide insight into how task teaching can be structured to make task learning easier and faster for people, suggesting improved methods for training and education. It will also increase our understanding of how a broad range of capabilities we associate with cognition work together, including extracting task-relevant meaning from perception, task-relevant action, grounded language processing, dialog and interaction management, integrated knowledge-rich relational reasoning, problem solving, learning, and metacognition. This integration contrasts the general trend in many relevant fields, which is toward increasing fragmentation and focusing on narrow problems.

The time is ripe for a deep exploration of the topic of how humans and artificial agents can quickly learn completely new tasks through natural interactions. It is a research problem that needs to draw from many disciplines that are often isolated because of different, although related, goals and very different methodologies. This Forum provides a unique opportunity to provide a foundational reference and organizing framework for global research and development in interactive task learning.

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Group 1: Interaction for Task Instruction and Learning

Central Question: What are the most effective and natural methods for humans, robots, and AI agents to interact in support of instruction and learning?

Here the focus is on interactive task learning, where a person, robot, or AI agent learns a task from another entity, typically a human teacher, through natural interaction. The interaction can include the teacher describing the goals and steps of the task using natural language, possibly leading the learner through the task; or the interaction can include the teacher demonstrating the task and the student acquiring new task knowledge and skill by observing what the teacher is doing. The interaction can include sketching, gesturing, or using other visual aids and non-verbal communications that must be interpreted in context and with regard to task goals. In addition, the interaction can include combinations of these modes, such as when the instructor provides a demonstration with natural language commentary. Interactive learning can also involve the student asking questions or requesting clarifications or additional examples in order to refine and improve understanding.

  • What are the most effective and efficient types of interactions for humans to use when instructing AI agents? How do human interactions with AI agents differ from interactions with humans?
  • What perceptual, cognitive, and motor capabilities do agents need in order to fully participate in and take advantage of these interactions and support human-teacher interactions (such as the ability to explain why an action was taken)?
  • How close to human-level language processing and understanding do an agent’s capabilities have to be so that a human can successfully teach an agent new tasks and skills? How can the agent be a “good student” so that it is easy for a human to teach and so that it only asks for help when it really needs it?
  • How can the many communication modalities be brought together to support a general interactive capability?
  • Which social sensitivities regarding initiative, turn-taking, and feedback are critical for successful implementation of artificial interaction?
  • Is there an optimal grain-size or time-scale for the implementation of interaction?
  • What are the limits of the interaction-based approach to task learning? Which types of tasks are best learned through other approaches, such as non-interaction-based experience?

Group 2: Task Knowledge

Central Question: What knowledge needs to be learned to acquire a novel task, and what existing background knowledge does an agent need so that it can effectively use that newly acquired knowledge?

The purpose of interactive task learning is for an agent to learn the knowledge necessary to perform well on a new task. Task competence is directly related to the concept of “understanding” as defined by Simon (1977): “S understands task T if S has the knowledge and procedures needed to perform T.” Learning to perform new tasks does not occur in isolation. We use knowledge of subtasks and skills to build on that prior knowledge when learning new tasks.  We also have existing knowledge about the structure of tasks, so we already know in the abstract what must be learned for a new task. Once that knowledge is learned, we also have general task-independent reasoning, problem solving, and planning capabilities that we can marshal to apply the task knowledge to perform the task. Task competence also includes general task management abilities such as supporting the pursuit of multiple tasks, interrupting low priority tasks with higher priority tasks, and resuming suspended tasks. Furthermore, being able to perform the task is only the first step in mastering the task. An agent should be able to acquire and learn additional knowledge through instruction, as well as its own experience, so that it achieves mastery of the task.

Another fundamental challenge for a task learning agent is that although it might know about the structure of tasks in general, it still needs to learn the specifics of many different tasks, whose details it may know little if anything about. Thus, it has to be able to learn diverse types of concepts (objects, categories, relations), procedures (hierarchical, recursive, interruptible), and goals (achievement, maintenance, process). Although specialized agents may be capable of learning specific types of tasks (such as puzzles and games, or procedure-based tasks), the ultimate goal is to understand what is required for an agent to learn all types of tasks.

  • What is the nature of an agent's existing background knowledge so that it can easily learn new tasks?
  • What are useful taxonomies of tasks and environments that could serve an organizing role for research in this area?
  • What general, fundamental capabilities and underlying cognitive architecture does an agent need in order to perform a given set of task types?
  • What are the types of knowledge that are necessary for an agent (and a human) to formulate different types of tasks for different environments?
  • How do the knowledge requirements and communication demands differ across task types?
  • What are the characteristics of the knowledge that can (and cannot) be obtained through interactive task learning?
  • How do environmental characteristics and task demands provide context to facilitate specification and generalization processes in task learning?
  • What shared materials can be developed (task domains, instruction materials, etc.) so that different research groups can effectively and efficiently evaluate their progress in relation to other research groups?

Group 3: Learning Task Knowledge

Central Question: What are the computational processes for assimilating and accommodating the diversity of new task knowledge through natural interaction with a human?

The primary goal of an interactive task learner is to learn a task from its interactions with a teacher and from its own experiences. It must have the necessary reasoning and learning capabilities to interpret instructions, map them onto the current situation, extract information about the task, generalize from examples and demonstrations, store experiences in its memories for future use, and then retrieve them when appropriate. What makes this especially challenging, in comparison to most research on machine learning, is that the learning does not occur within the confines of a specific task, where the learning mechanisms can be optimized to learn specific types of knowledge. In learning a new task, an agent must learn many different types of knowledge, from different types of interactions with an instructor, and it must learn quickly. No human instructor will stand for giving scores of examples: the agent must extract the relevant knowledge during the interactive session, and not through extensive off-line analysis. Moreover, learning must be online and integrated with the agent’s ongoing activities, so that at any time it can learn new aspects of tasks as well as interrupt learning based on demands of its existing goals and task.

  • What learning mechanisms are appropriate for interactive task learning?
  • How are learning mechanisms integrated into the other ongoing activities of an agent that is actively engaged in performing (and learning) a task?
  • How is the knowledge about a new task integrated with existing knowledge (assimilation), and how does existing knowledge change in light of new instruction (accommodation)?
  • How can an agent use its own background knowledge and reasoning mechanisms so that it can figure out “obvious” aspects of a task on its own, and engage in interactive learning only when necessary?
  • What primitive knowledge does an agent require to support task learning?
  • How can an agent generalize what it learns in one situation to other, similar situations?
  • What are the appropriate metrics for evaluating interactive task learning?
  • How can we compare agent learning in interactive task learning to human learning in similar situations? Are there existing task learning scenarios for human learning (child or adult) that can be replicated?

Group 4: Task Instruction

Central Question: What instructional principles enable and improve interactive task learning?

Interactive task learning differs from traditional instructional contexts, such as education and training, where the stability of the domains affords development of detailed, carefully crafted curricula. Teachers, trainers, and tutors usually are required to have a relevant advanced degree or expert proficiency before being allowed to instruct in education and training environments. By contrast, the point of interactive task learning is to quickly acquire competence from an available instructor who may be skilled in the task, but may not be skilled in instruction. The task may also be new for the instructor, such as when a person creates a new game, a new technology becomes available, or a creative, new means of accomplishing a previously known task is discovered and needs to be transmitted to a learner.  The requirement for a higher level of dynamism and in situ flexibility may impose special requirements on how instruction unfolds or these characteristics may enable specific affordances that can be taken advantage of by either the learner or instructor. We expect that we can draw on the lessons learned from research in education, training, expert systems, and intelligent tutoring.

  • What are the different instruction styles used by humans? This involves an analysis of expectations that human teachers have toward their students, and vice versa.
  • How much can we extract from best practices in the design of educational curricula and training regimens?
  • What information does an instructor need about a learner in order to be effective? How can that knowledge be made available, quickly and naturally?
  • How can the learner help the instructor provide effective instruction?
  • What is the state of the art in learning assessment and can we use that in interactive task learning?
  • What is involved if we flip the locus of instructional responsibility from a human to a robot or agent?
  • Can safety, security, and ethical operating parameters be assured in agents that are designed to learn to do entirely new things after they leave the controlled confines of their production environment? 
  • How can we guard against malicious teaching of falsehoods and nefarious efforts to teach robots and agents to do harm?
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Allen, J., N. Chambers, G. Ferguson, L. Galescu, H. Jung, M. Swift, and W. Taysom. 2007. PLOW: A Collaborative Task Learning Agent. In: Proc. Conf. on Artificial Intelligence (AAAI), vol. 22, pp. 1514. Menlo Park: AAAI Press.

Argall, B. D., S. Chernova, M. Veloso, and B. Browning. 2009. A Survey of Robot Learning from Demonstration. Robotics and Autonomous Systems 57(5):469–483.

Cakmak, M., and A. L. Thomaz. 2012. Designing Robot Learners that Ask Good Questions. In: Proc. 7th Annual ACM/IEEE Intl. Conf. on Human-Robot Interaction, pp. 17–24.

Gluck, K. A., and R. W. Pew, eds. 2005. Modeling Human Behavior with Integrated Cognitive Architectures: Comparison, Evaluation, and Validation. Mahwah, NJ: Erlbaum.

Hinrich, T. R., and K. D. Forbus. 2014. X Goes First: Teaching Simple Games through Multimodal Interaction. Advances in Cognitive Systems 3:31–46.

Kaiser, Ł. 2012. Learning Games from Videos Guided by Descriptive Complexity. In: Proc. 26th AAAI Conf. on Artificial Intelligence, pp. 963–970. AAAI Press.

Kirk, J., and J. E. Laird. 2013. Learning Task Formulations through Situated Interactive Instruction. In: Proc. 2nd Conf. on Advances in Cognitive Systems, pp. 219–236. Baltimore, MD: ACS.

Petit, M., Lallee, S., Boucher, J.-D., Pointeau, G., Cheminade, P., Ognibene, D., Chinellato, E., Pattacini, U., Gori, I., Martinez-Hernandez, U., Barron-Gonzalez, H., Inderbitzin, M., Luvizotto, A., Vouloutsi, V., Demiris, Y., Metta, G., Dominey, P.F. 2013. The Coordinating Role of Language in Real-Time Multimodal Learning of Cooperative Tasks. IEEE Transactions on Autonomous Mental Development 5(1):3–17.

Simon, H. A. 1977. Artificial intelligence systems that understand. Proc. 5th Intl. Joint Conf. on Artificial Intelligence, vol. 2, pp. 1059–1073. San Francisco: Morgan Kaufmann Publ.

Taatgen, N. A. 2013. The nature and transfer of cognitive skills. Psychological Review 120(3):439–471.

VanLehn, K. 2011. The relative effectiveness of human tutoring, intelligent tutoring systems and other tutoring systems. Educational Psychologist 46(4):197-221.