I have worked in the area of artificial intelligence, and specifically on challenges in machine learning and data mining, for twenty years. Originally these challenges focused on theoretical and algorithmic issues. Eventually, I became interested in applying these ideas to complex, real-world problems. Applied AI and machine learning not only allows researchers like me to see tangible benefits of the work, but it also introduces new algorithmic and theoretical challenges that need to be tackled.
As AI algorithms scale, they no longer exist just in the virtual world but find use in the real world. The result is that intelligent agents not only need to focus on their own problems but need to interact with other agents. As this book discusses, these agents may be components of a single system. Alternatively, they may be independent agents that are cooperating in order to solve a larger problem or they may actually be competing for resources. The agents may be pieces of software or they could be physical beings such as humans or robots. An intelligent agent may automatically discover a clever way to negotiate with others and such an agent may even harness the capabilities of other agents to boost its own performance.
I met Sajal Das, one of the editors of this book, when we both worked at the University of Texas at Arlington. Sajal is an expert in mobile computing, wireless networks, pervasive and distributed systems and has written numerous books, conference and journal articles ...