While founded on the situation calculus, current implementations of Golog are mainly based on the closed-world assumption or its dynamic versions or the domain closure assumption. Also, they are almost exclusively based on regression. In this paper, we propose a first-order interpreter for knowledge-based Golog with sensing based on exact progression and limited reasoning. We assume infinitely many unique names and handle first-order disjunctive information in the form of the so-called proper+ KBs. Our implementation is based on the progression and limited reasoning algorithms for proper+ KBs proposed by Liu, Lakemeyer and Levesque. To improve efficiency, we implement the two algorithms by grounding via a trick based on the unique name assumption. The interpreter is online but the programmer can use two operators to specify offline execution for parts of programs. The search operator returns a conditional plan, while the planning operator is used when local closed-world information is available and calls a modern planner to generate a sequence of actions.

Hence, we are mainly In the last three decades, Answer Set Programming (ASP) focused on (anti)monotone aggregates. Even for this case, has emerged as a predominant declarative programming the task is still very complicated as aggregate atoms, on one paradigm in the area of knowledge representation and logic hand, can express some features of existential quantifiers, programming (Baral 2003). One of the main focuses of recent and on the other hand, contribute to the loops (Chen et al. advances in ASP is first-order answer set programming 2006; Lee and Meng 2009) of the program.

High dimensional nearest neighbor search is a fundamental problem and has found applications in many domains. Although many hashing based approaches have been proposed for approximate nearest neighbor search in high dimensional space, one main drawback is that they often return many false positives that need to be filtered out by a post procedure. We propose a novel method to address this limitation in this paper. The key idea is to introduce a filtering procedure within the search algorithm, based on the compressed sensing theory, that effectively removes the false positive answers. We first obtain a sparse representation for each data point by the landmark based approach, after which we solve the nearly duplicate search that the difference between the query and its nearest neighbors forms a sparse vector living in a small ℓp ball, where p ≤ 1. Our empirical study on real-world datasets demonstrates the effectiveness of the proposed approach compared to the state-of-the-art hashing methods.

Influence maximization is a problem of finding a small set of highly influential users in a social network such that the spread of influence under certain propagation models is maximized. In this paper, we consider time-critical influence maximization, in which one wants to maximize influence spread within a given deadline. Since timing is considered in the optimization, we also extend the Independent Cascade (IC) model to incorporate the time delay aspect of influence diffusion in social networks. We show that time-critical influence maximization under the time-delayed IC model maintains desired properties such as submodularity, which allows a greedy algorithm to achieve an approximation ratio of 1-1/e, to circumvent the NP-hardness of the problem. To overcome the inefficiency of the approximation algorithm, we design two heuristic algorithms: the first one is based on a dynamic programming procedure that computes exact influence in tree structures, while the second one converts the problem to one in the original IC model and then applies existing fast heuristics to it. Our simulation results demonstrate that our heuristics achieve the same level of influence spread as the greedy algorithm while running a few orders of magnitude faster, and they also outperform existing algorithms that disregard the deadline constraint and delays in diffusion.

Scott Turner's 1993 Minstrel system was a high water mark in story generation, harnessing the concept of imaginative recall to generate creative stories. Using case-based reasoning and an author level planning system, Minstrel models human creative processes. However, the algorithmic and representational commitments made in Minstrel were never subject to principled and quantitative analysis. By rationally reconstructing Minstrel, we are able to investigate Turner's computational model of creativity and learn new lessons about his architecture. We find that Minstrel's original performance was tied to a well-groomed case library, but by modifying several components of the algorithm we can create a more general version which can construct stories using a sparser and less structured case library. Through a rational reconstruction of Minstrel, we both learn new architectural and algorithmic lessons about Minstrel’s computational model of creativity as well as make his architecture available to the contemporary research community for further experimentation.

An important difference between traditional AI systems and human intelligence is our ability to harness common sense knowledge gleaned from a lifetime of learning and experiences to inform our decision making and behavior. This allows humans to adapt easily to novel situations where AI fails catastrophically for lack of situation-specific rules and generalization capabilities. Common sense knowledge also provides the background knowledge for humans to successfully operate in social situations where such knowledge is typically assumed. In order for machines to exploit common sense knowledge in reasoning as humans do, moreover, we need to endow them with human-like reasoning strategies. In this work, we propose a two-level affective reasoning framework that concurrently employs multi-dimensionality reduction and graph mining techniques to mimic the integration of conscious and unconscious reasoning, and exploit it for sentiment analysis.

Automatic Subjective Question Answering (ASQA), which aims at answering users'subjective questions using summaries of multiple opinions, becomes increasingly important. One challenge of ASQA is that expected answers for subjective questions may not readily exist in the Web. The rising and popularity of Community Question Answering (CQA) sites, which provide platforms for people to post and answer questions, provides an alternative to ASQA. One important task of ASQA is question subjectivity identification, which identifies whether a user is asking a subjective question. Unfortunately, there has been little labeled training data available for this task. In this paper, we propose an approach to collect training data automatically by utilizing social signals in CQA sites without involving any manual labeling. Experimental results show that our data-driven approach achieves 9.37% relative improvement over the supervised approach using manually labeled data, and achieves 5.15% relative gain over a state-of-the-art semi-supervised approach. In addition, we propose several heuristic features for question subjectivity identification. By adding these features, we achieve 11.23% relative improvement over word n-gram feature under the same experimental setting.

Comparing entities is an important part of decision making. Several approaches have been reported for mining comparable entities from Web sources to improve user experience in comparing entities online.However, these efforts extract only entities explicitly compared in the corpora, and may exclude entities that occur less-frequently but potentially comparable. To build a more complete comparison machine that can infer such missing relations, here we develop a solutionto predict transitivity of known comparable relations. Named CliqueGrow, our approach predicts missing links given a comparable entity graph obtained from versus query logs. Our approach achieved the highest F1-score among five link prediction approaches and a commercial comparison engine provided by Yahoo!.

With the exponential increase in the number of documents available online, e.g., news articles, weblogs, scientific documents, the development of effective and efficient classification methods is needed. The performance of document classifiers critically depends, among other things, on the choice of the feature representation. The commonly used "bag of words" and n-gram representations can result in prohibitively high dimensional input spaces. Data mining algorithms applied to these input spaces may be intractable due to the large number of dimensions. Thus, dimensionality reduction algorithms that can process data into features fast at runtime, ideally in constant time per feature, are greatly needed in high throughput applications, where the number of features and data points can be in the order of millions. One promising line of research to dimensionality reduction is feature clustering. We propose to combine two types of feature clustering, namely hashing and abstraction based on hierarchical agglomerative clustering, in order to take advantage of the strengths of both techniques. Experimental results on two text data sets show that the combined approach uses significantly smaller number of features and gives similar performance when compared with the "bag of words" and n-gram approaches.

We consider the problem of designing automated strategies for interactions with human subjects, where the humans must be rewarded for performing certain tasks of interest. We focus on settings where there is a single task that must be performed many times by different humans (e.g. answering a questionnaire), and the humans require a fee for performing the task. In such settings, our objective is to minimize the average cost for effectuating the completion of the task. We present two automated strategies for designing efficient agents for the problem, based on two different models of human behavior. The first, the Reservation Price Based Agent (RPBA), is based on the concept of a reservation price, and the second, the No Bargaining Agent (NBA), uses principles from behavioral science. The performance of the agents has been tested in extensive experiments with real human subjects, where NBA outperforms both RPBA and strategies developed by human experts.