Multi-task Learning for Software Agents

This paper describes experiments done to demonstrate the effectiveness of Multi-task Learning (MTL) for software agents. The experiments were carried out on an agent for processing electronic mail (email). MTL is shown to slightly improve the learning rate in this domain over Single-task Learning (STL) in a k-nearest-neighbor implementation. We then introduce a new method of lazy learning we call Neural Neighbor which lends itself much better to the incorporation of MTL and outperforms both our STL and MTL k-nearest-neighbor implementations. 1 Introduction This paper deals with the effectiveness of Multi-task Learning techniques for software agents. Software agents are computer programs with the ability to customize themselves automatically to a specific user. That is, a software agent learns and modifies its behavior by observing the way it is being used by a human. Many types of software agents have been developed, including agents to maintain personal calendars [24], predict intere..

FASTER MUSE CSP ARC CONSISTENCY ALGORITHMS

MUSE CSP (Multiply SEgmented Constraint Satisfaction Problem) [5, 61 is an extension to the constraint satisfaction problem (CSP) which is especially useful for problems that segment into riultiple instances of CSP that share variables. In Belzerman and Harper [6], the concepts of MUSE node, arc, and path consistency were defined and algorithms for MUSE arc consistency, MUSE AC-1, and MUSE path consistency were developed. MUSE AC-1 is similar to the CSP arc consistency algorithm AC-4 [ l j ] . Recently, Bessikre developed a new algorithm, AC-6 [I], which has the same worst-case running time as AC-4 and is faster than AC-3 and AC-4 in practice. In this paper, we focus on developing two faster MUSE arc consistency algor~thms:M USE AC-2 which directly applies Bessikre\u27s method to improve upon MUSE AC-1, and MUSE AC-3, which uses our new lazy evaluation method for keeping track of the additional sets required by the MUSE approach. These new algorithms decrease the number of steps required to achieve arc ccnsistency in randomly generated MUSE CSP instances when compared to MUSE AC-1. Keyvrords: Problem Solving, Constraint Satisfaction, MUSE arc consistency