A practical roadmap to human-level artificial general intelligence is outlined, leading from the current situation in which complex AGI-oriented cognitive architectures remain partially implemented and inadequately tested, to a future in which AGI systems are deployed to carry out a variety of practical tasks currently only achievable via humans. The roadmap involves simultaneous development of proto-AGI applications in a number of different areas: videogame agent control, mobile robotics, genomics, natural language dialogue, theorem proving and program learning. The simultaneous pursuit of diverse application areas using a single AGI architecture is proposed as critical to maintaining generality of intelligence throughout the development process. While acknowledging the profound difficulty of time estimation for a project of this complexity, it is projected that the roadmap outlined could potentially be completed in a decade or less.

The hypothesis is presented that "cognitive synergy" -- proactive and mutually-assistive feedback between different cognitive processes associated with different types of memory -- may serve as a foundation for advanced artificial general intelligence. A specific AI architecture founded on this idea, OpenCogPrime, is described, in the context of its application to control virtual agents and robots. The manifestations of cognitive synergy in OpenCogPrime's procedural and declarative learning algorithms are discussed in some detail.

Interest management is essential for scalable collaborative virtual environments (CVEs) which sought to reduce bandwidth consumption on the network. Most of the interest management systems such as Data Distribution Management (DDM) service of the High Level Architecture (HLA) concentrate on providing precise message filtering mechanisms. However, in doing so a second problem is introduced: the CPU cycle overheads of filtering process. If the cost in terms of computational resources of interest management itself is too high, it would be unsuitable for real time applications such as multiplayer online games (MOGs) for which runtime performance is important. In this paper we present a scalable interest management algorithm which is suitable for HLA DDM. Our approach employs the collision detection method of I-COLLIDE for fast interest matching. Furthermore, the algorithm has been implemented in our commercialized MOG middleware - Lucid Platform. Experimental evidence demonstrates that it works well in practice.