This paper tackles the problem of building situated prompting and assistance systems for guiding a human with a cognitive disability through a large domain containing multiple tasks. This problem is challenging because the target population has difficulty maintaining goals, recalling necessary steps and recognizing objects and potential actions (affordances), and therefore may not appear to be acting rationally. Prompts or cues from an automated system can be very helpful in this regard, but the domain is inherently partially observable due to sensor noise and uncertain human behaviours, making the task of selecting an appropriate prompt very challenging. Prior work has shown how such automated assistance for a single task can be modeled as a partially observable Markov decision process (POMDP). In this paper, we generalise this to multiple tasks, and show how to build a scalable, distributed and hierarchical controller. We demonstrate the algorithm in a set of simulated domains and show it can perform as well as the full model in many cases, and can give solutions to large problems (over 10 15 states and 10 9 observations) for which the full model fails to find a policy.

Bayesian Network Classifiers are popular approaches for classification problems where instances have to be assigned to one of several classes. However, in many domains, it is necessary to assign instances to multiple classes at the same time. This task has been normally addressed either by (i) transforming the problem into a single-class scenario by defining a new class variable with all of the possible combinations of classes or, (ii) by building an independent classifier for each class variable. Either way, the resulting models do not capture all the relations and dependencies between classes and features resulting into unprecise multidimensional classifiers. In this paper, we introduce a two-step method for learning Multidimensional Bayesian Network Classifiers (MBC) from data based on mutual information measures. The first step of the method learns an initial MBC structure which then, in the second step, is refined. Our approach is simple and keeps all the interactions and dependencies among classes and features. The method was tested on three benchmark multidimensional data-sets. Preliminary experimental results show how our method outperforms state-of-the-art methods used in multidimensional classification.

For a knowledge-based system that fails to provide the correct answer, it is important to be able to tune the system while minimizing overall change in the knowledge-base. There are a variety of reasons why the answer is incorrect ranging from incorrect knowledge to information vagueness to incompleteness. Still, in all these situations, it is typically the case that most of the knowledge in the system is likely to be correct as specified by the expert(s) and/or knowledge engineer(s). In this paper, we propose a method to identify the possible changes by understanding the contribution of parameters on the outputs of concern. Our approach is based on Bayesian Knowledge Bases for modeling uncertainties. We start with single parameter changes and then extend to multiple parameters. In order to identify the optimal solution that can minimize the change to the model as specified by the domain experts, we define and evaluate the sensitivity values of the results with respect to the parameters. We discuss the computational complexities of determining the solution and show that the problem of multiple parameters changes can be transformed into Linear Programming problems, and thus, efficiently solvable. Our work can also be applied towards validating the knowledge base such that the updated model can satisfy all test-cases collected from the domain experts.

The Partially Observable Markov Decision Process (POMDP) provides a rich mathematical model for designing agents that have to formulate plans under uncertainty. The curses of dimensionality and history associated with solving POMDPs have lead to numerous refinements of the value iteration algorithm. Several exact methods with different pruning strategies have been devised, yet, limited scalability has lead research to focus on ways to approximate the optimal value function. One set of approximations relies on point-based value iteration, which maintains a fixed-size value function, and is typically executed offline. Another set of approximations relies on tree search, which explores the implicit tree defined by the value iteration equation, and is typically executed online. In this paper we present a hybrid value iteration algorithm that combines the benefits of point-based value iteration and tree search. Using our approach, a hybrid agent executes tree search online, and occasionally updates its offline-computed lower bound on the optimal value function, resulting in improved lookahead and higher obtained reward, while meeting real-time constraints. Thus, unlike other hybrid algorithms that use an invariant value function computed offline, our proposed scheme uses information from the real-time tree search process to reason whether to perform a point-based backup online. Keeping track of partial results obtained during online planning makes the computation of point-based backups less prohibitive. We report preliminary results that support our approach.

Temporal Nodes Bayesian Networks (TNBNs) are an alternative to Dynamic Bayesian Networks for temporal reasoning, that result in much simpler and efficient models in some domains. However, methods for learning this type of models from data have not been developed. In this paper we propose a learning algorithm to obtain the structure and temporal intervals for TNBNs from data. The method has three phases: (i) obtain an initial approximation of the intervals, (ii) obtain a structure using a standard algorithm and (iii) refine the intervals for each temporal node based on a clustering algorithm. We evaluated the method with synthetic data. Our method obtains the best score in terms of the structure and a competitive predictive accuracy.

Causality plays an important role in our comprehension of the world. It amounts to determine what truly causes what and what it matters. Interventions allow the identification of elements in a sequence of events that are related in a causal way. In this paper, we introduce belief causation and we proposea method for handling interventions in graphical model under an uncertain environment where the uncertainty is represented by belief masses, so-called belief causal networks. More specifically, we propose a generalization of the “DO” operator and explain the needed changes on the structure of the graph to model a belief causal network on which interventions are proceeded.

Creating natural language tutorial dialogue systems that realize effective strategies is a central challenge for intelligent tutoring systems research. Traditional approaches generally require large development time, do not generalize well across domains, and do not match the flexibility and natural language sophistication of human tutors. A promising approach that may offer several benefits is data-driven system development, in which a dialogue policy is learned from corpora of human tutorial dialogue. To date these learning approaches typically focus on optimizing the tutor’s choice of act, and do not explicitly model the instances in which the tutor chose not to act. This paper reports on a hidden Markov modeling (HMM) approach within human textual tutorial dialogue that explicitly represents the tutors’ choices not to intervene. The results show that an HMM that models tutor non-interventions predicts tutor moves significantly better than a model that does not explicitly represent the non-interventions. The findings have implications for automatically modeling tutorial strategies and for learning dialogue policies from corpora.

Analyzing chat traffic has important applications for both the military and the civilian world. This poster will report on an effort to automatically separate chat messages into topic threads.

Dialogue-based intelligent tutoring systems use speech act classifiers to categorize student input into answers, questions, and other speech acts. Previous work has primarily focused on question classification. In this paper, we present a complimentary speech act classifier that focuses primarily on non-questions, which was developed using machine learning techniques. Our results show that an effective speech act classifier can be developed directly from labeled data using decision trees.

Given the diversity and spatio-temporal scales of dynamic coastal processes, sampling is a challenging task for oceanographers. To meet this challenge new robotic platforms such as Autonomous Underwater Vehicle (AUV) are being increasingly used. For effective water sampling during a mission an AUV should be adaptive to its environment, which requires it to be able to identify these dynamic and episodic ocean features in-situ. We describe the use of Hidden Markov Models (HMM) as a feature detection model used onboard an AUV, an autonomous untethered robot. We show how to build an identification model from data collected during past missions. Then we show how the parameters of the HMM can be optimized using a Genetic Algorithm approach, from models trained with the Baum-Welch algorithm in the initial population.