Picbreeder: A Case Study in Collaborative Evolutionary Exploration of Design Space

For domains in which fitness is subjective or difficult to express formally, interactive evolutionary computation (IEC) is a natural choice. It is possible that a collaborative process combining feedback from multiple users can improve the quality and quantity of generated artifacts. Picbreeder, a large-scale online experiment in collaborative interactive evolution (CIE), explores this potential. Picbreeder is an online community in which users can evolve and share images, and most importantly, continue evolving others\u27 images. Through this process of branching from other images, and through continually increasing image complexity made possible by the underlying neuroevolution of augmenting topologies (NEAT) algorithm, evolved images proliferate unlike in any other current IEC system. This paper discusses not only the strengths of the Picbreeder approach, but its challenges and shortcomings as well, in the hope that lessons learned will inform the design of future CIE systems

Plan Ahead: Pricing Its Learner Models

Intelligent tutoring systems (ITSs) are highly adapted to individual learners, and therefore their learner models are central to their operation and account for a large fraction of their development costs. Different learner model architectures may have different development costs, but those costs are not widely reported in the literature. This paper presents individual reports from an anonymous questionnaire sent to ITS professionals in September 2009. The respondents estimated the development costs of recent ITSs and their associated learner models. The resulting data aligns with and amplifies published accounts, as well as contributing new cost information about model types that have not previously appeared in the literature

Tractable Pomdp Representations For Intelligent Tutoring Systems

With Partially Observable Markov Decision Processes (POMDPs), Intelligent Tutoring Systems (ITSs) can model individual learners from limited evidence and plan ahead despite uncertainty. However, POMDPs need appropriate representations to become tractable in ITSs that model many learner features, such as mastery of individual skills or the presence of specific misconceptions. This article describes two POMDP representations-state queues and observation chains-that take advantage of ITS task properties and let POMDPs scale to represent over 100 independent learner features. A real-world military training problem is given as one example. A human study (n = 14) provides initial validation for the model construction. Finally, evaluating the experimental representations with simulated students helps predict their impact on ITS performance. The compressed representations can model a wide range of simulated problems with instructional efficacy equal to lossless representations. With improved tractability, POMDP ITSs can accommodate more numerous or more detailed learner states and inputs. © 2013 ACM

Integrating Learner Help Requests Using A Pomdp In An Adaptive Training System

This paper describes the development and empirical testing of an intelligent tutoring system (ITS) with two emerging methodologies: (1) a partially observable Markov decision process (POMDP) for representing the learner model and (2) inquiry modeling, which informs the learner model with questions learners ask during instruction. POMDPs have been successfully applied to non-ITS domains but, until recently, have seemed intractable for large-scale intelligent tutoring challenges. New, ITS-specific representations leverage common regularities in intelligent tutoring to make a POMDP practical as a learner model. Inquiry modeling is a novel paradigm for informing learner models by observing rich features of learners\u27 help requests such as categorical content, context, and timing. The experiment described in this paper demonstrates that inquiry modeling and planning with POMDPs can yield significant and substantive learning improvements in a realistic, scenario-based training task. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved

What Information Does This Question Convey? Leveraging Help-Seeking Behavior For Improved Modeling In A Simulation-Based Intelligent Tutor

Asking questions is an important help-seeking behavior that many intelligent tutoring systems (ITSs) do not use. Allowing learners to ask questions of an ITS has the potential to improve learning and also to provide a new source of input for ITSs\u27 internal models. In this paper, the different ways an ITS can input questions, answer them, and then use them to update its student model are discussed. A taxonomy of question response for model-based learning environments is proposed, and inquiry modeling, a new framework to let learners ask questions of an ITS with more freedom than existing methods, is described. Inquiry modeling is being developed and tested in a popular military training simulation, the DVTE-CAN. © 2010 SCS

Scalable Pomdps For Diagnosis And Planning In Intelligent Tutoring Systems

A promising application area for proactive assistant agents is automated tutoring and training. Intelligent tutoring systems (ITSs) assist tutors and tutees by automating diagnosis and adaptive tutoring. These tasks are well modeled by a partially observable Markov decision process (POMDP) since it accounts for the uncertainty inherent in diagnosis. However, an important aspect of making POMDP solvers feasible for real-world problems is selecting appropriate representations for states, actions, and observations. This paper studies two scalable POMDP state and observation representations. State queues allow POMDPs to temporarily ignore less-relevant states. Observation chains represent information in independent dimensions using sequences of observations to reduce the size of the observation set. Preliminary experiments with simulated tutees suggest the experimental representations perform as well as lossless POMDPs, and can model much larger problems. Copyright © 2010, Association for the Advancement of Artificial Intelligence. All rights reserved