Protein secondary structure prediction plays a pivotal role in predicting protein folding in three-dimensions. Its task is to assign each residue one of the three secondary structure classes helix, strand, or random coil. This is an instance of the problem of sequential supervised learning in machine learning. This thesis describes...
The thesis focuses on model-based approximation methods for reinforcement
learning with large scale applications such as combinatorial optimization problems.
First, the thesis proposes two new model-based methods to stablize the
value–function approximation for reinforcement learning. The first one is the
BFBP algorithm, a batch-like reinforcement learning process which iterates between...
Domain-independent automated planning is concerned with computing a sequence of actions that can transform an initial state into a desired goal state. Resource production domains form an interesting class of such problems, in that they typically require reasoning about concurrent durative-actions with continuous effects while minimizing some cost function. Although...
Image feature detection and matching are two critical processes for many computer vision tasks. Currently, intensity-based local interest region detectors and local feature-based matching methods are used widely in computer vision applications. But in some applications, such as biological object recognition tasks, within-class changes in pose, lighting, color, and texture...
Current ACI design provisions for shear in reinforced concrete (RC) beams strengthened
with externally bonded carbon fiber-reinforced polymer (CFRP) U-wraps may not adequately account for the effect of scale. This paper describes tests of 6 geometrically scaled beams to identify possible scale effects of such beams. Results indicated that effective...
This thesis addresses the problem of learning dynamic Bayesian network (DBN) models to support reinforcement learning. It focuses on learning regression tree models of the conditional probability distributions of the DBNs. Existing algorithms presume that the stochasticity in the domain can be modeled as a deterministic function with additive noise....
We consider the problem of tactical assault planning in real-time strategy games where a team of friendly agents must launch an assault on an enemy. This problem offers many challenges including a highly dynamic and uncertain environment, multiple agents, durative actions, numeric attributes, and different optimization objectives. While the dynamics...
Automated recognition of object categories in images is a critical step for many real-world computer vision applications. Interest region detectors and region descriptors have been widely employed to tackle the variability of objects in pose, scale, lighting, texture, color, and so on. Different types of object recognition problems usually require...
Knowledge workers are struggling in the information flood. There is a growing interest in intelligent desktop environments that help knowledge workers organize their daily life. Intelligent desktop environments allow the desktop user to define a set of “activities” that characterize the user’s desktop work. These environments then attempt to identify...
Recently, the concept of performance based design has become popular for many types of structures, including port facilities under seismic loading. For the case of pile-supported wharves, the level of performance is generally estimated using the displacement capacity of the structure. Therefore, understanding soil-pile interaction is one of the most...