The ways in which an agent’s actions affect the world can often be modeled compactly using a set of relational probabilistic planning rules. This paper addresses the problem of learning such rule sets for multiple related tasks. We take a hierarchical Bayesian approach, in which the system learns a prior distribution over rule sets. We present a class of prior distributions parameterized by a r...

This paper de nes and addresses the competence and capability assess ment problem A simple method based on the meta knowledge available in a reasoning system is given that can be used to assess competence This idea is applied to the planner ucpop to assess capability in the Blocks World domain Several theoretical and practical problems lead to some experiments that involve generating partial se...

Our work is intended to model and solve artificial planning problems with logic based planning, using the novel semantics called p-stable, which is an alternative of stable semantics. Also we present a method to encode a general planning problem and then we present an example, which is the blocks world problem. This can be applied in a variety of tasks including robotics, process planning, upda...

In this paper we revisit the line size/performance trade-offs in off-chip second-level caches in light of energy efficiency and make two distinct contributions: Our first observation is that while large blocks, i.e., 128-256 bytes, typically improve performance, they cause a devastating energy dissipation because the limited spatial locality results in a low block utilization. We find that bloc...

Our aim was to optimize a SOA control system by evolving the architecture of the service component that transforms system models into task trees, which are then executed by the runtime library called the Task Tree Executor, TTE. In the paper we present the two novel TTE architectures that evolved from the previous TTE architecture and introduced finer grained parallelism. The novel architecture...

Upscaling based on the bandwidth of the kernel function is a flexible approach to upscale the data because the cells will be coarse-based on variability. The intensity of the coarsening of cells in this method can be controlled with bandwidth. In a smooth variability region, a large number of cells will be merged, and vice versa, they will remain fine with severe variability. Bandwidth variatio...