We propose the structured naive Bayes (SNB) classifier, which augments the ubiquitous naive Bayes classifier with structured features. SNB classifiers facilitate the use of complex features, such as combinatorial objects (e.g., graphs, paths and orders) in a general but systematic way. Underlying the SNB classifier is the recently proposed Probabilistic Sentential Decision Diagram (PSDD), which is a tractable representation of probability distributions over structured spaces. We illustrate the utility and generality of the SNB classifier via case studies. First, we show how we can distinguish players of simple games in terms of play style and skill level based purely on observing the games they play. Second, we show how we can detect anomalous paths taken on graphs based purely on observing the paths themselves.

We consider optimal route planning when the objective function is a general nonlinear and non-monotonic function. Such an objective models user behavior more accurately, for example, when a user is risk-averse, or the utility function needs to capture a penalty for early arrival. It is known that as non-linearity arises, the problem can become NP-hard and little is known on computing optimal solutions when in addition there is no monotonicity guarantee. We show that an approximately optimal non-simple path can be efficiently computed under some natural constraints. In particular, we provide a fully polynomial approximation scheme under hop constraints. Our approximation algorithm can extend to run in pseudo-polynomial time under an additional linear constraint that sometimes is useful. As a by-product, we show that our algorithm can be applied to the problem of finding a path that is most likely to be on time for a given deadline.

The global growth in urbanisation increases the demand for services including road transport infrastructure, presenting challenges in terms of mobility. In this scenario, optimising the exploitation of urban road networks is a pivotal challenge. Existing urban traffic control approaches, based on complex mathematical models, can effectively deal with planned-ahead events, but are not able to cope with unexpected situations --such as roads blocked due to car accidents or weather-related events-- because of their huge computational requirements. Therefore, such unexpected situations are mainly dealt with manually, or by exploiting pre-computed policies. Our goal is to show the feasibility of using mixed discrete-continuous planning to deal with unexpected circumstances in urban traffic control. We present a PDDL+ formulation of urban traffic control, where continuous processes are used to model flows of cars, and show how planning can be used to efficiently reduce congestion of specified roads by controlling traffic light green phases. We present simulation results on two networks (one of them considers Manchester city centre) that demonstrate the effectiveness of the approach, compared with fixed-time and reactive techniques.

Goal Recognition Design involves identifying the best ways to modify an underlying environment that agents operate in, typically by making asubset of feasible actions infeasible, so that agents are forced to reveal their goals as early as possible. Thus far, existing work has focused exclusively on imperative classical planning. In this paper, we address the same problem with a different paradigm, namely, declarative approaches based on Answer Set Programming (ASP). Our experimental results show that one of our ASP encodings is more scalable and is significantly faster by up to three orders of magnitude than thecurrent state of the art.

We study transportation problems where robots have to deliver packages and can transfer the packages among each other. Specifically, we study the package-exchange robot-routing problem (PERR), where each robot carries one package, any two robots in adjacent locations can exchange their packages, and each package needs to be delivered to a given destination. We prove that exchange operations make all PERR instances solvable. Yet, we also show that PERR is NP-hard to approximate within any factor less than 4/3 for makespan minimization and is NP-hard to solve for flowtime minimization, even when there are only two types of packages. Our proof techniques also generate new insights into other transportation problems, for example, into the hardness of approximating optimal solutions to the standard multi-agent path-finding problem (MAPF). Finally, we present optimal and suboptimal PERR solvers that are inspired by MAPF solvers, namely a flow-based ILP formulation and an adaptation of conflict-based search. Our empirical results demonstrate that these solvers scale well and that PERR instances often have smaller makespans and flowtimes than the corresponding MAPF instances.

Partially Observable Markov Decision Processes (POMDPs) are often used to model planning problems under uncertainty. The goal in Risk-Sensitive POMDPs (RS-POMDPs) is to find a policy that maximizes the probability that the cumulative cost is within some user-defined cost threshold. In this paper, unlike existing POMDP literature, we distinguish between the two cases of whether costs can or cannot be observed and show the empirical impact of cost observations. We also introduce a new search-based algorithm to solve RS-POMDPs and show that it is faster and more scalable than existing approaches in two synthetic domains and a taxi domain generated with real-world data.

We consider recently-derived error bounds that can be used to bound the quality of solutions found by heuristic search algorithms for stochastic shortest path problems. In their original form, the bounds can only be used for problems with positive action costs. We show how to generalize the bounds so that they can be used in solving any stochastic shortest path problem, regardless of cost structure. In addition, we introduce a simple new heuristic search algorithm that performs as well or better than previous algorithms for this class of problems, while being easier to implement and analyze.

As the amount of knowledge bases (KBs) grows rapidly, the problem of question answering (QA) over multiple KBs has drawn more attention. The most significant distinction between multiple KB-QA and single KB-QA is that the former must consider the alignments between KBs. The pipeline strategy first constructs the alignments independently, and then uses the obtained alignments to construct queries. However, alignment construction is not a trivial task, and the introduced noises would be passed on to query construction. By contrast, we notice that alignment construction and query construction are interactive steps, and jointly considering them would be beneficial. To this end, we present a novel joint model based on integer linear programming (ILP), uniting these two procedures into a uniform framework. The experimental results demonstrate that the proposed approach outperforms state-of-the-art systems, and is able to improve the performance of both alignment construction and query construction.

The task of tweet timeline generation (TTG) aims at selecting a small set of representative tweets to generate a meaningful timeline and providing enough coverage for a given topical query. This paper presents an approach based on determinantal point processes (DPPs) by jointly modeling the topical relevance of each selected tweet and overall selectional diversity. Aiming at better treatment for balancing relevance and diversity, we introduce two novel strategies, namely spectral rescaling and topical prior. Extensive experiments on the public TREC 2014 dataset demonstrate that our proposed DPP model along with the two strategies can achieve fairly competitive results against the state-of-the-art TTG systems.

Continuously discovering novel entities in news and Web data is important for Knowledge Base (KB) maintenance. One of the key challenges is to decide whether an entity mention refers to an in-KB or out-of-KB entity. We propose a principled approach that learns a novel entity classifier by modeling mention and entity representation into multiple feature spaces, including contextual, topical, lexical, neural embedding and query spaces. Different from most previous studies that address novel entity discovery as a submodule of entity linking systems, our model is more a generalized approach and can be applied as a pre-filtering step of novel entities for any entity linking systems. Experiments on three real-world datasets show that our method significantly outperforms existing methods on identifying novel entities.