An important requirement of household energy simulation models is their accuracy in estimating energy demand and its fluctuations. Occupant behavior has a major impact upon energy demand. However, Markov chains, the traditional approach to model occupant behavior, (1) has limitations in accurately capturing the coordinated behavior of occupants and (2) is prone to over-fitting. To address these issues, we propose a novel approach that relies on a combination of data mining techniques. The core idea of our model is to determine the behavior of occupants based on nearest neighbor comparison over a database of sample data. Importantly, the model takes into account features related to the coordination of occupants' activities. We use a customized distance function suited for mixed categorical and numerical data. Further, association rule learning allows us to capture the coordination between occupants. Using real data from four households in Japan we are able to show that our model outperforms the traditional Markov chain model with respect to occupant coordination and generalization of behavior patterns.

This paper focuses on computing general first-order parallel and prioritized circumscription with varying constants. We propose linear translations from general first-order circumscription to first-order theories under stable model semantics over arbitrary structures, including Tr_v for parallel circumscription and Tr^s_v for conjunction of parallel circumscriptions (further for prioritized circumscription). To improve the efficiency, we give an optimization \Gamma_{\exists} to reduce logic programs in size when eliminating existential quantifiers during the translations. Based on these results, a general first-order circumscription solver, named cfo2lp, is developed by calling answer set programming (ASP) solvers. Using circuit diagnosis problem and extended stable marriage problem as benchmarks, we compare cfo2lp with a propositional circumscription solver circ2dlp and an ASP solver with complex optimization metasp on efficiency. Experimental results demonstrate that for problems represented by first-order circumscription naturally and intuitively, cfo2lp can compute all solutions over finite structures. We also apply our approach to description logics with circumscription and repairs in inconsistent databases, which can be handled effectively.

In this paper we consider the setting of graph-structured data that evolves as a result of operations carried out by users or applications. We study different reasoning problems, which range from ensuring the satisfaction of a given set of integrity constraints after a given sequence of updates, to deciding the (non-)existence of a sequence of actions that would take the data to an (un)desirable state, starting either from a specific data instance or from an incomplete description of it. We consider a simple action language in which actions are finite sequences of insertions and deletions of nodes and labels, and use Description Logics for describing integrity constraints and (partial) states of the data. We then formalize the data management problems mentioned above as a static verification problem and several planning problems. We provide algorithms and tight complexity bounds for the formalized problems, both for an expressive DL and for a variant of DL-Lite.

The Fisher market model is one of the most fundamental resource allocation models in economics. In a Fisher market, the prices and allocations of goods are determined according to the preferences and budgets of buyers to clear the market. In a Fisher market game, however, buyers are strategic and report their preferences over goods; the market-clearing prices and allocations are then determined based on their reported preferences rather than their real preferences. We show that the Fisher market game always has a pure Nash equilibrium, for buyers with linear, Leontief, and Cobb-Douglas utility functions, which are three representative classes of utility functions in the important Constant Elasticity of Substitution (CES) family. Furthermore, to quantify the social efficiency, we prove Price of Anarchy bounds for the game when the utility functions of buyers fall into these three classes respectively.

The Game Description Language GDL is the standard input language for general game-playing systems. While players can gain a lot of traction by an efficient inference algorithm for GDL, state-of-the-art reasoners suffer from a variant of a classical KR problem, the inferential frame problem. We present a method by which general game players can transform any given game description into a representation that solves this problem. Our experimental results demonstrate that with the help of automatically generated domain knowledge, a significant speedup can thus be obtained for the majority of the game descriptions from the AAAI competition.

We study techniques to incentivize self-interested agents to form socially desirable solutions in scenarios where they benefit from mutual coordination. Towards this end, we consider coordination games where agents have different intrinsic preferences but they stand to gain if others choose the same strategy as them. For non-trivial versions of our game, stable solutions like Nash Equilibrium may not exist, or may be socially inefficient even when they do exist. This motivates us to focus on designing efficient algorithms to compute (almost) stable solutions like Approximate Equilibrium that can be realized if agents are provided some additional incentives. Our results apply in many settings like adoption of new products, project selection, and group formation, where a central authority can direct agents towards a strategy but agents may defect if they have better alternatives. We show that for any given instance, we can either compute a high quality approximate equilibrium or a near-optimal solution that can be stabilized by providing small payments to some players. Our results imply that a little influence is necessary in order to ensure that selfish players coordinate and form socially efficient solutions.

Mesoscale ocean eddies are a critical component of the Earth System as they dominate the ocean's kinetic energy and impact the global distribution of oceanic heat, salinity, momentum, and nutrients. Therefore, accurately representing these dynamic features is critical for our planet's sustainability. The majority of methods that identify eddies from satellite observations analyze the data in a frame-by-frame basis despite the fact that eddies are dynamic objects that propagate across space and time. We introduce the notion of spatio-temporal consistency to identify eddies in a continuous spatio-temporal field, to simultaneously ensure that the features detected are both spatially and temporally consistent. Our spatio-temporal consistency approach allows us to remove most of the expert criteria used in traditional methods to reduce false negatives. The removal of arbitrary heuristics enables us to render more complete eddy dynamics by identifying smaller and longer lived eddies compared to existing methods.

Although users' preference is semantically reflected in the free-form review texts, this wealth of information was not fully exploited for learning recommender models. Specifically, almost all existing recommendation algorithms only exploit rating scores in order to find users' preference, but ignore the review texts accompanied with rating information. In this paper, we propose a novel matrix factorization model (called TopicMF) which simultaneously considers the ratings and accompanied review texts. Experimental results on 22 real-world datasets show the superiority of our model over the state-of-the-art models, demonstrating its effectiveness for recommendation tasks.

In this work, we propose a novel node splitting method for regression trees and incorporate it into the regression forest framework. Unlike traditional binary splitting, where the splitting rule is selected from a predefined set of binary splitting rules via trial-and-error, the proposed node splitting method first finds clusters of the training data which at least locally minimize the empirical loss without considering the input space. Then splitting rules which preserve the found clusters as much as possible are determined by casting the problem into a classification problem. Consequently, our new node splitting method enjoys more freedom in choosing the splitting rules, resulting in more efficient tree structures. In addition to the Euclidean target space, we present a variant which can naturally deal with a circular target space by the proper use of circular statistics. We apply the regression forest employing our node splitting to head pose estimation (Euclidean target space) and car direction estimation (circular target space) and demonstrate that the proposed method significantly outperforms state-of-the-art methods (38.5% and 22.5% error reduction respectively).

Multi-label learning concerns learning multiple, overlapping, and correlated classes. In this paper, we adapt a recent structured prediction framework called HC-Search for multi-label prediction problems. One of the main advantages of this framework is that its training is sensitive to the loss function, unlike the other multi-label approaches that either assume a specific loss function or require a manual adaptation to each loss function. We empirically evaluate our instantiation of the HC-Search framework along with many existing multi-label learning algorithms on a variety of benchmarks by employing diverse task loss functions. Our results demonstrate that the performance of existing algorithms tends to be very similar in most cases, and that the HC-Search approach is comparable and often better than all the other algorithms across different loss functions.