In this paper we consider planning problems in relationalMarkov processes where objects may "appear" or "disap-pear", perhaps depending on previous actions or propertiesof other objects. For instance, problems which require to ex-plicitly generate or discover objects fall into this category. Inour formulation this requires to explicitly represent the un-certainty over the number of objects (dimensions or factors)in a dynamic Bayesian networks (DBN). Many formalisms(also existing ones) are conceivable to formulate such prob-lems. We aim at a formulation that facilitates inference andplanning. Based on a specific formulation we investigate twoinference methods--rejection sampling and reversible-jumpMCMC--to compute a posterior over the process conditionedon the first and last time slice (start and goal state).

Mobile robots deployed in complex real-world domains typically find it difficult to process all sensor inputs or operate without substantial domain knowledge. At the same time, humans may not have the time and expertise to provide elaborateand accurate knowledge or feedback. The architecture described in this paper combines declarative programming and probabilistic sequential decision-making to address these challenges. Specifically, Answer Set Programming (ASP), a declarative programming paradigm, is combined with hierarchical partially observable Markov decision processes (POMDPs), enabling robots to: (a) represent and reason with incomplete domain knowledge, revising existing knowledge using information extracted from sensor inputs; (b) probabilistically model the uncertainty in sensor input processing and navigation; and (c) use domain knowledge to revise probabilistic beliefs, exploiting positive and negative observations to identify situations in which the assigned task can no longer be pursued. All algorithms are evaluated in simulation and on mobile robots locating target objects in indoor domains.

The Partially-Observable Markov Decision Process (POMDP) is a general framework to determine reward-maximizing action policies under noisy action and sensing conditions. However, determining an optimal policy for POMDPs is often intractable for robotic tasks due to the PSPACE-complete nature of the computation required. Several recent solvers have been introduced that expand the size of problems that can be considered. Although these POMDP solvers can respect complex motion constraints in theory, we show that the computational cost does not provide a benefit in the eventual online execution, compared to our alternative approach that relies on a policy that ignores some of the motion constraints. We advocate using the POMDP framework where it is critical -- to find a policy that provides the optimal action given all past noisy sensor observations, while abstracting some of the motion constraints to reduce solution time. However, the actions of an abstract robot are generally not executable under its true motion constraints. The problem is addressed offline with a less-constrained POMDP, and navigation under the full system constraints is handled online with replanning. We empirically demonstrate that the policy generated using this abstracted motion model is faster to compute and achieves similar or higher reward than addressing the motion constraints for a car-like robot as used in our experiments directly in the POMDP.

In this paper we address the task of extracting risk events and probabilities from free text, focusing in particular on the biomedical domain. While our initial motivation is to enable the determination of the parameters of a Bayesian belief network, our approach is not specific to that use case. We are the first to investigate this task as a sequence tagging problem where we label spans of text as events A or B that are then used to construct probability statements of the form P(A B) x. We show that our approach significantly outperforms an entity extraction baseline on a new annotated medical risk event corpus. We also explore semi-supervised methods that lead to modest improvement, encouraging further work in this direction.

In order to successfully interact with multiple humans in social situations, an intelligent robot should have the ability to track multi-humans, and understand their motion intentions. We formalize this problem as a hidden Markov model, and estimate the posterior densities by particle filtering over sets approach. Our approach avoids directly performing observation-to-target association by defining a set as a joint state. The human identification problem is then solved in an expectation-maximization way. We evaluate the effectiveness of our approach by both benchamark test and real robot experiments.

We parallelize the Point-Based Value Iteration (PBVI) algorithm, which approximates the solution to Partially Observable Markov Decision Processes (POMDPs), using a Graphics Processing Unit (GPU).

External factors are hard to model using a Markovian state in several real-world planning domains. Although planning can be difficult in such domains, it may be possible to exploit long-term dependencies between states of the environment during planning. We introduce weighted state scenarios to model long-term sequences of states, and we use a model based on a Partially Observable Markov Decision Process to reason about scenarios during planning. Experiments show that our model outperforms other methods for decision making in two real-world domains.

Issuing and following instructions is a common task in many forms of both human-human and human-robot collaboration. With two human participants, the accuracy of instruction following increases if the collaborators can monitor the state of their partners and respond to them through conversation (Clark and Krych 2004), a process we call social feedback. Despite this benefit in human-human interaction, current human-robot collaboration systems process instructions in non-incremental batches, which can achieve good accuracy but does not allow for reactive feedback (Tellex et al. 2011; Matuszek et al. 2012; Tellex et al. 2012; Misra et al.2014). In this paper, we show that giving a robot the ability to ask the user questions results in responsive conversations and allows the robot to quickly determine the object that the user desires. This social feedback loop between person and robot allows a person to create an internal model for the robot's mental state and adapt their own behavior to better inform the robot. To close the human-robot feedback loop, we employ a Partially Observable Markov Decision Process (POMDP) to produce a policy which will lead to the determination of the object in the shortest amount of time. To test our approach, we perform user studies to measure our robot's ability to deliver common household items requested by the participant. We compare delivery speed and accuracy both with and without social feedback.

Although we would like our robots to have completely autonomous behavior, this is often not possible. Some parts of a task might be hard to automate, perhaps due to hard-to-interpret sensor information, or a complex environment. In this case, using shared autonomy or teleoperation is preferable to an error-prone autonomous approach. However, the question of which parts of a task to allocate to the human, and which to the robot can often be tricky. In this work, we introduce A3P, a risk-aware task-level reinforcement learning algorithm. A3P represents a task-level state machine as a POMDP. In this paper, we introduce A3P, a risk-aware algorithm that discovers when to hand off subtasks to a human assistant. A3P models the task as a Partially Observably Markov Decision Process (POMDP) and explicitly represents failures as additional state-action pairs. Based on the model, the algorithm allows the user to allocate subtasks the robot or the human in such a way as to manage the worst-case performance time for the overall task.

We study the relationship between performance and practice by analyzing the activity of many players of a casual online game. We find significant heterogeneity in the improvement of player performance, given by score, and address this by dividing players into similar skill levels and segmenting each player's activity into sessions, that is, sequence of game rounds without an extended break. After disaggregating data, we find that performance improves with practice across all skill levels. More interestingly, players are more likely to end their session after an especially large improvement, leading to a peak score in their very last game of a session. In addition, success is strongly correlated with a lower quitting rate when the score drops, and only weakly correlated with skill, in line with psychological findings about the value of persistence and "grit:" successful players are those who persist in their practice despite lower scores. Finally, we train an ε-machine, a type of hidden Markov model, and find a plausible mechanism of game play that can predict player performance and quitting the game. Our work raises the possibility of real-time assessment and behavior prediction that can be used to optimize human performance.