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SHOP2: An HTN Planning System
TLDR
The features of SHOP2 which enabled it to excel in the competition are described, especially those aspects of SHop2 that deal with temporal and metric planning domains. Expand
SUNNY: A New Algorithm for Trust Inference in Social Networks Using Probabilistic Confidence Models
TLDR
SUNNY is described, a new trust inference algorithm that uses a probabilistic sampling technique to estimate the authors' confidence in the trust information from some designated sources, and computes an estimate of trust based on only those information sources with high confidence estimates. Expand
Applications of SHOP and SHOP2
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, practicalExpand
HTN-MAKER: Learning HTNs with Minimal Additional Knowledge Engineering Required
TLDR
This work describes HTN-MAKER, an algorithm for learning hierarchical planning knowledge in the form of decomposition methods for Hierarchical Task Networks (HTNs), and presents a formalism for a class of planning problems that are more expressive than classical planning. Expand
Incremental plan aggregation for generating policies in MDPs
TLDR
A way to generate policies in MDPs by determinizing the given MDP model into a classical planning problem, and using sequential Monte-Carlo simulations of the partial policies before execution, in order to assess the probability of replanning for a policy during execution is described. Expand
Web Service Composition with Volatile Information
TLDR
Two ways to take Web service composition procedures and systematically modify them to deal with volatile information are described and it is shown theoretically that both approaches work correctly and experimental results are presented showing that the WSC procedures produced by the gray-box approach can run much faster than the onesproduced by the black- box approach. Expand
Using Classical Planners to Solve Nondeterministic Planning Problems
TLDR
This work provides a way to use these algorithms, unmodified, to generate strong-cyclic solutions in fully-observable nondeterministic planning domains, and shows that when using this technique with FF and SGPlan, its performance compares quite favorably to that of MBP. Expand
Forward-Chaining Planning in Nondeterministic Domains
TLDR
The results suggest that the general technique for taking forward-chaining planners for deterministic domains and adapting them to work in nondeterministic domains preserves many of the desirable properties of these planners, such as the ability to use heuristic techniques to achieve highly efficient planning. Expand
Using probabilistic confidence models for trust inference in Web-based social networks
TLDR
SUNNY is described, a new trust inference algorithm that uses probabilistic sampling to separately estimate trust information and the authors' confidence in the trust estimate and use the two values in order to compute an estimate of trust based on only those information sources with the highest confidence estimates. Expand
Using Friendship Ties and Family Circles for Link Prediction
TLDR
It is shown that when there are tightly-knit family circles in a social network, the accuracy of link prediction models can be improved, by making use of the family circle features based on the likely structural equivalence of family members. Expand
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