Intelligent robots frequently need to understand requests from naive users through natural language. Previous approaches either cannot account for language variation, e.g., keyword search, or require gathering large annotated corpora, which can be expensive and cannot adapt to new variation. We introduce a dialog agent for mobile robots that understands human instructions through semantic parsing, actively resolves ambiguities using a dialog manager, and incrementally learns from human-robot conversations by inducing training data from user paraphrases. Our dialog agent is implemented and tested both on a web interface with hundreds of users via Mechanical Turk and on a mobile robot over several days, tasked with understanding navigation and delivery requests through natural language in an office environment. In both contexts, We observe significant improvements in user satisfaction after learning from conversations.

Solving cooperative path finding (CPF) by translating it to propositional satisfiability represents a viable option in highly constrained situations. The task in CPF is to relocate agents from their initial positions to given goals in a collision free manner. In this paper, we propose a reduced time expansion that is focused on makespan sub-optimal solving. The suggested reduced time expansion is especially beneficial in conjunction with a goal decomposition where agents are relocated one by one.

This paper reports recent progress on modeling the grounded co-acquisition of syntax and semantics of locative spatial language in developmental robots. Weshow how a learner robot can learn to produce and interpret spatial utterances in guided-learning interactions with a tutor robot (equipped with a system for producing English spatial phrases). The tutor guides the learning process by simplifying the challenges and complexity of utterances, givesfeedback, and gradually increases the complexity of the language to be learnt. Our experiments show promising results towards long-term, incremental acquisition of natural language in a process of co-development of syntax and semantics.

Knowledge bases (KBs) are often greatly incomplete, necessitating a demand for KB completion. A promising approach is to embed KBs into latent spaces and make inferences by learning and operating on latent representations. Such embedding models, however, do not make use of any rules during inference and hence have limited accuracy. This paper proposes a novel approach which incorporates rules seamlessly into embedding models for KB completion. It formulates inference as an integer linear programming (ILP) problem, with the objective function generated from embedding models and the constraints translated from rules. Solving the ILP problem results in a number of facts which 1) are the most preferred by the embedding models, and 2) comply with all the rules. By incorporating rules, our approach can greatly reduce the solution space and significantly improve the inference accuracy of embedding models. We further provide a slacking technique to handle noise in KBs, by explicitly modeling the noise with slack variables. Experimental results on two publicly available data sets show that our approach significantly and consistently outperforms state-of-the-art embedding models in KB completion. Moreover, the slacking technique is effective in identifying erroneous facts and ambiguous entities, with a precision higher than 90%.

Existing techniques for inference in probabilistic logic programs are sequential: they first compute the relevant propositional formula for the query of interest, then compile it into a tractable target representation and finally, perform weighted model counting on the resulting representation. We propose Tp-compilation, a new inference technique based on forward reasoning. Tp-compilation proceeds incrementally in that it interleaves the knowledge compilation step for weighted model counting with forward reasoning on the logic program. This leads to a novel anytime algorithm that provides hard bounds on the inferred probabilities. Furthermore, an empirical evaluation shows that Tp-compilation effectively handles larger instances of complex real-world problems than current sequential approaches, both for exact and for anytime approximate inference.

We present Saul, a new probabilistic programming language designed to address some of the shortcomings of programming languages that aim at advancing and simplifying the development of AI systems. Such languages need to interact with messy, naturally occurring data, to allow a programmer to specify what needs to be done at an appropriate level of abstraction rather than at the data level, to be developed on a solid theory that supports moving to and reasoning at this level of abstraction and, finally, to support flexible integration of these learning and inference models within an application program. Saul is an object-functional programming language written in Scala that facilitates these by (1) allowing a programmer to learn, name and manipulate named abstractions over relational data; (2) supporting seamless incorporation of trainable (probabilistic or discriminative) components into the program, and (3) providing a level of inference over trainable models to support composition and make decisions that respect domain and application constraints. Saul is developed over a declaratively defined relational data model, can use piecewise learned factor graphs with declaratively specified learning and inference objectives, and it supports inference over probabilistic models augmented with declarative knowledge-based constraints.We describe the key constructs of Saul and exemplify its use in developing applications that require relational feature engineering and structured output prediction.

We study the problem of inducing logic programs in a probabilistic setting, in which both the example descriptions and their classification can be probabilistic. The setting is incorporated in the probabilistic rule learner ProbFOIL+, which combines principles of the rule learner FOIL with ProbLog, a probabilistic Prolog. We illustrate the approach by applying it to the knowledge base of NELL, the Never-Ending Language Learner.

Items in real-world recommender systems exhibit certain hierarchical structures. Similarly, user preferences also present hierarchical structures. Recent studies show that incorporating the explicit hierarchical structures of items or user preferences can improve the performance of recommender systems. However, explicit hierarchical structures are usually unavailable, especially those of user preferences. Thus, there's a gap between the importance of hierarchical structures and their availability. In this paper, we investigate the problem of exploring the implicit hierarchical structures for recommender systems when they are not explicitly available. We propose a novel recommendation framework HSR to bridge the gap, which enables us to capture the implicit hierarchical structures of users and items simultaneously. Experimental results on two real world datasets demonstrate the effectiveness of the proposed framework.

Tour recommendation and itinerary planning are challenging tasks for tourists, due to their need to select Points of Interest (POI) to visit in unfamiliar cities, and to select POIs that align with their interest preferences and trip constraints. We propose an algorithm called PersTour for recommending personalized tours using POI popularity and user interest preferences, which are automatically derived from real-life travel sequences based on geo-tagged photos. Our tour recommendation problem is modelled using a formulation of the Orienteering problem, and considers user trip constraints such as time limits and the need to start and end at specific POIs. In our work, we also reflect levels of user interest based on visit durations, and demonstrate how POI visit duration can be personalized using this time-based user interest. Using a Flickr dataset of four cities, our experiments show the effectiveness of PersTour against various baselines, in terms of tour popularity, interest, recall, precision and F1-score. In particular, our results show the merits of using time-based user interest and personalized POI visit durations, compared to the current practice of using frequency-based user interest and average visit durations.

Many robotic tasks require solutions that maximize multiple performance objectives. For example, in path-planning, these objectives often include finding short paths that avoid risk and maximize the information obtained by the robot. Although there exist many algorithms for multiobjective optimization, few of these algorithms apply directly to robotic path-planning and fewer still are capable of finding the set of Pareto optimal solutions. We present the MORRF*(Multi-Objective Rapidly exploring Random Forest*) algorithm, which blends concepts from two different types of algorithms from the literature: Optimal rapidly exploring random tree (RRT*) for efficient path finding and a decomposition-based approach to multi-objective optimization. The random forest uses two types of tree structures: a set of reference trees and a set of subproblem trees. We present a theoretical analysis that demonstrates that the algorithm asymptotically produces the set of Pareto optimal solutions, and use simulations to demonstrate the effectiveness and efficiency of MORRF* in approximating the Pareto set.