Automating the construction of semantic grammars is a di cult and interesting problem for machine learning. This paper shows how the semantic-grammar acquisition problem can be vi...
Learning to recognize or predict sequences using long-term context has many applications. However, practical and theoretical problems are found in training recurrent neural networ...
We investigate planning for self-interested agents in large multi-agent simulations. We present two heuristic algorithms that exploit different domain-specific properties in order...
A systematic understanding of the decomposability structures in network utility maximization is key to both resource allocation and functionality allocation. It helps us obtain the...
Many-objective problems are difficult to solve using conventional multi-objective evolutionary algorithms (MOEAs) as these algorithms rely primarily on Pareto ranking to guide the ...