Ugur Kuter

Bio: Ugur Kuter is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Hierarchical task network & Nondeterministic algorithm. The author has an hindex of 24, co-authored 78 publications receiving 2806 citations.

SHOP2: an HTN planning system

Abstract: The SHOP2 planning system received one of the awards for distinguished performance in the 2002 International Planning Competition. This paper describes the features of SHOP2 which enabled it to excel in the competition, especially those aspects of SHOP2 that deal with temporal and metric planning domains.

SUNNY: a new algorithm for trust inference in social networks using probabilistic confidence models

Abstract: In many computing systems, information is produced and processed by many people. Knowing how much a user trusts a source can be very useful for aggregating, filtering, and ordering of information. Furthermore, if trust is used to support decision making, it is important to have an accurate estimate of trust when it is not directly available, as well as a measure of confidence in that estimate. This paper describes a new approach that gives an explicit probabilistic interpretation for confidence in social networks. We describe SUNNY, a new trust inference algorithm that uses a probabilistic sampling technique to estimate our confidence in the trust information from some designated sources. SUNNY computes an estimate of trust based on only those information sources with high confidence estimates. In our experiments, SUNNY produced more accurate trust estimates than the well known trust inference algorithm TIDALTRUST (Golbeck 2005), demonstrating its effectiveness.

Applications of SHOP and SHOP2

Abstract: We design the simple hierarchical ordered planner (SHOP) and its successor, SHOP2, with two goals in mind: to investigate research issues in automated planning and to provide some simple, practical planning tools. SHOP and SHOP2 are based on a planning formalism called hierarchical task network planning. SHOP and SHOP2 use a search-control strategy called ordered task decomposition, which breaks tasks into subtasks and generates the plan's actions in the same order that the plan executor executes them. So, throughout the planning process, the planner can tell what the state of the world at each step of the plan.

Using friendship ties and family circles for link prediction

Abstract: Social networks can capture a variety of relationships among the participants. Both friendship and family ties are commonly studied, but most existing work studies them in isolation. Here, we investigate how these networks can be overlaid, and propose a feature taxonomy for link prediction. We show that when there are tightly-knit family circles in a social network, we can improve the accuracy of link prediction models. This is done by making use of the family circle features based on the likely structural equivalence of family members. We investigated the predictive power of overlaying friendship and family ties on three real-world social networks. Our experiments demonstrate significantly higher prediction accuracy (between 15% and 30% more accurate) compared to using more traditional features such as descriptive node attributes and structural features. The experiments also show that a combination of all three types of attributes results in the best precision-recall trade-off.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Recommender systems survey

Abstract: Recommender systems have developed in parallel with the web. They were initially based on demographic, content-based and collaborative filtering. Currently, these systems are incorporating social information. In the future, they will use implicit, local and personal information from the Internet of things. This article provides an overview of recommender systems as well as collaborative filtering methods and algorithms; it also explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.

Link prediction in complex networks: A survey

Abstract: Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods. We also introduce three typical applications: reconstruction of networks, evaluation of network evolving mechanism and classification of partially labeled networks. Finally, we introduce some applications and outline future challenges of link prediction algorithms.

Bringing semantics to web services: the OWL-S approach

Abstract: Service interface description languages such as WSDL, and related standards, are evolving rapidly to provide a foundation for interoperation between Web services. At the same time, Semantic Web service technologies, such as the Ontology Web Language for Services (OWL-S), are developing the means by which services can be given richer semantic specifications. Richer semantics can enable fuller, more flexible automation of service provision and use, and support the construction of more powerful tools and methodologies. Both sets of technologies can benefit from complementary uses and cross-fertilization of ideas. This paper shows how to use OWL-S in conjunction with Web service standards, and explains and illustrates the value added by the semantics expressed in OWL-S.