proposed in recent years. This paper offers a task by data type taxonomy with seven data types (one-, two-, three-dimensional datu, temporal and multi-dimensional data, and tree and network data) and seven tasks (overview, Zoom, filter, details-on-demand, relate, history, and extracts).

task, but differ completely at the levels of synaptic strengths and single-unit responses. However, hierarchical clustering techniques applied to NETtalk reveal that these different networks have similar internal representations of letter-to-sound correspondences within groups of processing units

be accommodated by a fixed-resource system? Using a prediction task, a simple recurrent network (SRN) is trained on multiclausal sentences which contain multiply-embedded relative clauses. Principal component analysis of the hidden unit activation patterns reveals that the network solves the task by developing

Most practical work on AI planning systems during the last fteen years has been based on hierarchical task network (HTN) decomposition, but until now, there has been very little analytical work on the properties of HTN planners. This paper describes how the complexity of HTN planning varies

is first translated into an alternative representation called multivalued planning tasks, which makes many of the implicit constraints of a propositional planning task explicit. Exploiting this alternative representation, Fast Downward uses hierarchical decompositions of planning tasks for computing its

One big obstacle to understanding the nature of hierarchical task network (htn) planning has been the lack of a clear theoretical framework. In particular, no one has yet presented a clear and concise htn algorithm that is sound and complete. In this paper, we present a formal syntax and semantics

One big obstacle to understanding the nature of hierarchical task network (htn) planning has been the lack of a clear theoretical framework. In particular, no one has yet presented a clear and concise htn algorithm that is sound and complete.

in the recognized action sequence is analyzed, which results in a hierarchical task plan describing the higher level structure of the task. In another workspace with a different initial state, the system re-instantiates and executes the task plan to accomplish an equivalent goal. The effectiveness of our method

higher level, and 4) models of the world and memories of events expand their range in space and time by about an order-of-magnitude at each higher level. At each level, functional modules perform behavior generation (task decomposition planning and execution), world modeling, sensory processing

The up-coming Gbps high-speed networks are expected to support a wide range of communication-intensive, real-time multimedia applications. The requirement for timely delivery of digitized audio-visual information raises new challenges for the next generation integrated-service broadband networks