Modeling Human Understanding of Complex Intentional Action with a Bayesian Nonparametric Subgoal Model

Most human behaviors consist of multiple parts, steps, or subtasks. These structures guide our action planning and execution, but when we observe others, the latent structure of their actions is typically unobservable, and must be inferred in order to learn new skills by demonstration, or to assist others in completing their tasks. For example, an assistant who has learned the subgoal structure of a colleague's task can more rapidly recognize and support their actions as they unfold. Here we model how humans infer subgoals from observations of complex action sequences using a nonparametric Bayesian model, which assumes that observed actions are generated by approximately rational planning over unknown subgoal sequences. We test this model with a behavioral experiment in which humans observed different series of goal-directed actions, and inferred both the number and composition of the subgoal sequences associated with each goal. The Bayesian model predicts human subgoal inferences with high accuracy, and significantly better than several alternative models and straightforward heuristics. Motivated by this result, we simulate how learning and inference of subgoals can improve performance in an artificial user assistance task. The Bayesian model learns the correct subgoals from fewer observations, and better assists users by more rapidly and accurately inferring the goal of their actions than alternative approaches.Comment: Accepted at AAAI 1

Simulation Studies of the NLC with Improved Ground Motion Models

The performance of various systems of the Next Linear Collider (NLC) have been studied in terms of ground motion using recently developed models. In particular, the performance of the beam delivery system is discussed. Plans to evaluate the operation of the main linac beam-based alignment and feedback systems are also outlined.Comment: Submitted to XX International Linac Conferenc

Modeling Human Ad Hoc Coordination

Whether in groups of humans or groups of computer agents, collaboration is most effective between individuals who have the ability to coordinate on a joint strategy for collective action. However, in general a rational actor will only intend to coordinate if that actor believes the other group members have the same intention. This circular dependence makes rational coordination difficult in uncertain environments if communication between actors is unreliable and no prior agreements have been made. An important normative question with regard to coordination in these ad hoc settings is therefore how one can come to believe that other actors will coordinate, and with regard to systems involving humans, an important empirical question is how humans arrive at these expectations. We introduce an exact algorithm for computing the infinitely recursive hierarchy of graded beliefs required for rational coordination in uncertain environments, and we introduce a novel mechanism for multiagent coordination that uses it. Our algorithm is valid in any environment with a finite state space, and extensions to certain countably infinite state spaces are likely possible. We test our mechanism for multiagent coordination as a model for human decisions in a simple coordination game using existing experimental data. We then explore via simulations whether modeling humans in this way may improve human-agent collaboration.Comment: AAAI 201

Supporting parent-child conversations in a history museum

BACKGROUND: Museums can serve as rich resources for families to learn about the social world through engagement with exhibits and parent-child conversation about exhibits. AIMS: This study examined ways of engaging parents and child about two related exhibits at a cultural and history museum. Sample participants consisted of families visiting the Animal Antics and the Gone Potty exhibits at the British Museum. METHODS: Whilst visiting two exhibits at the British Museum, 30 families were assigned to use a backpack of activities, 13 were assigned to a booklet of activities, and 15 were assigned to visit the exhibits without props (control condition). RESULTS: Compared to the families in the control condition, the interventions increased the amount of time parents and children engaged together with the exhibit. Additionally, recordings of the conversations revealed that adults asked more questions related to the exhibits when assigned to the two intervention conditions compared to the control group. Children engaged in more historical talk when using the booklets than in the other two conditions. CONCLUSIONS: The findings suggest that providing support with either booklets or activities for children at exhibits may prove beneficial to parent-child conversations and engagement with museum exhibits

Disentangling Factors of Variation with Cycle-Consistent Variational Auto-Encoders

Generative models that learn disentangled representations for different factors of variation in an image can be very useful for targeted data augmentation. By sampling from the disentangled latent subspace of interest, we can efficiently generate new data necessary for a particular task. Learning disentangled representations is a challenging problem, especially when certain factors of variation are difficult to label. In this paper, we introduce a novel architecture that disentangles the latent space into two complementary subspaces by using only weak supervision in form of pairwise similarity labels. Inspired by the recent success of cycle-consistent adversarial architectures, we use cycle-consistency in a variational auto-encoder framework. Our non-adversarial approach is in contrast with the recent works that combine adversarial training with auto-encoders to disentangle representations. We show compelling results of disentangled latent subspaces on three datasets and compare with recent works that leverage adversarial training

Concurrent Verbal Protocol Analysis in Sport: Illustration of Thought Processes during a Golf-Putting task

The purpose of this study was to examine the feasibility of concurrent verbal protocols to identify and map thought processes of players during a golf-putting task. Three novice golfers and three experienced golfers performed twenty 12-foot putts while thinking aloud. Verbalizations were transcribed verbatim and coded using an inductive method. Content analysis and event-sequence analysis were performed. Mapping of thought sequences indicated that experienced players’ cog­nitive processes centered on gathering information and planning, while beginners focused on technical aspects. Experienced players diagnosed current performance aspects more often than beginners did and were more likely to use this informa­tion to plan the next putt. These results are consistent with experienced players’ higher domain-specific knowledge and less reliance on step-by-step monitoring of motor performance than beginners. The methods used for recording, analyzing, and interpreting on-line thoughts of performers shed light on cognitive processes, which have implications for research

Beam-based Feedback Simulations for the NLC Linac

Extensive beam-based feedback systems are planned as an integral part of the Next Linear Collider (NLC) control system. Wakefield effects are a significant influence on the feedback design, imposing both architectural and algorithmic constraints. Studies are in progress to assure the optimal selection of devices and to refine and confirm the algorithms for the system design. We show the results of initial simulations, along with evaluations of system response for various conditions of ground motion and other operational disturbances.Comment: 3 pages. Linac2000 conferenc

Glial Cell Line-Derived Neurotrophic Factor Gene Delivery in Parkinson's Disease: A Delicate Balance between Neuroprotection, Trophic Effects, and Unwanted Compensatory Mechanisms.

Glial cell line-derived neurotrophic factor (GDNF) and Neurturin (NRTN) bind to a receptor complex consisting of a member of the GDNF family receptor (GFR)-α and the Ret tyrosine kinase. Both factors were shown to protect nigro-striatal dopaminergic neurons and reduce motor symptoms when applied terminally in toxin-induced Parkinson's disease (PD) models. However, clinical trials based on intraputaminal GDNF protein administration or recombinant adeno-associated virus (rAAV)-mediated NRTN gene delivery have been disappointing. In this review, several factors that could have limited the clinical benefits are discussed. Retrograde transport of GDNF/NRTN to the dopaminergic neurons soma is thought to be necessary for NRTN/GFR-α/Ret signaling mediating the pro-survival effect. Therefore, the feasibility of treating advanced patients with neurotrophic factors is questioned by recent data showing that: (i) tyrosine hydroxylase-positive putaminal innervation has almost completely disappeared at 5 years post-diagnosis and (ii) in patients enrolled in the rAAV-NRTN trial more than 5 years post-diagnosis, NRTN was almost not transported to the substantia nigra pars compacta. In addition to its anti-apoptotic and neurotrophic properties, GDNF also interferes with dopamine homeostasis via time and dose-dependent effects such as: stimulation of dopamine neuron excitability, inhibition of dopamine transporter activity, tyrosine hydroxylase phosphorylation, and inhibition of tyrosine hydroxylase transcription. Depending on the delivery parameters, the net result of this intricate network of regulations could be either beneficial or deleterious. In conclusion, further unraveling of the mechanism of action of GDNF gene delivery in relevant animal models is still needed to optimize the clinical benefits of this new therapeutic approach. Recent developments in the design of regulated viral vectors will allow to finely adjust the GDNF dose and period of administration. Finally, new clinical studies in less advanced patients are warranted to evaluate the potential of AAV-mediated neurotrophic factors gene delivery in PD. These will be facilitated by the demonstration of the safety of rAAV administration into the human brain