Agents In The Long Game Of Ai
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Agents in the Long Game of AI
A novel approach to hybrid AI aimed at developing trustworthy agent collaborators. The vast majority of current AI relies wholly on machine learning (ML). However, the past thirty years of effort in this paradigm have shown that, despite the many things that ML can achieve, it is not an all-purpose solution to building human-like intelligent systems. One hope for overcoming this limitation is hybrid AI: that is, AI that combines ML with knowledge-based processing. In Agents in the Long Game of AI, Marjorie McShane, Sergei Nirenburg, and Jesse English present recent advances in hybrid AI with special emphases on content-centric computational cognitive modeling, explainability, and development methodologies. At present, hybridization typically involves sprinkling knowledge into an ML black box. The authors, by contrast, argue that hybridization will be best achieved in the opposite way: by building agents within a cognitive architecture and then integrating judiciously selected ML results. This approach leverages the power of ML without sacrificing the kind of explainability that will foster society’s trust in AI. This book shows how we can develop trustworthy agent collaborators of a type not being addressed by the “ML alone” or “ML sprinkled by knowledge” paradigms—and why it is imperative to do so.
The Very Long Game
This open access book is the outcome of a unique multinational effort organized by the Hamburg-based Defense AI Observatory (DAIO) to portray the current state of affairs regarding the use of artificial intelligence (AI) by armed forces around the world. The contributions span a diverse range of geostrategic contexts by providing in-depth case studies on Australia, Canada, China, Denmark, Estonia, Finland, France, Germany, Greece, India, Iran, Israel, Italy, Japan, the Netherlands, Russia, Singapore, South Korea, Spain, Sweden, Taiwan, Turkey, Ukraine, the UK, and the United States. The book does not speculate about the future implications of AI on armed forces, but rather discusses how armed forces are currently exploring the potential of this emerging technology. By adopting a uniform analytical framework, each case study discusses how armed forces view defense AI; how they are developing AI-enhanced solutions, adapting existing structures and processes, and funding their defense AI endeavors; to what extent defense AI is already fielded and operated; and how soldiers and officers are being trained to work with AI.
Agents for Games and Simulations II
While today's game engines and multi-agent platforms cross-fertilize each other to some extent, the technologies used in these areas are not readily compatible due to some differences in their primary concerns. Where game engines prioritize efficiency and central control, multi-agent platforms focus on agent autonomy and sophisticated communication capabilities. This volume gives an overview of the current state of the art for people wishing to combine agent technology with (serious) games. This state-of-the-art survey contains a collection of papers presented at AGS 2010; the Second International Workshop on Agents for Games and Simulations, held on May 10, 2010, in Toronto, as well as extended versions of papers from other workshops and from the AAMAS conference. The 14 papers are organized in three topical sections focusing on architectures combining agents and game engines, on the training aspects of the games, on social and organizational aspects of games and agents, respectively.