In this essay we claim that computing is the fourth great scientific domain, on par with the physical, life, and social sciences.

doi:10.1093/comjnl/bxs070 Comp J 2012; 55(7): 820–824 In this article, there were spelling errors in the introductory sentence. This should have appeared as: In this essay we claim that computing is the fourth great scientific domain, on par with the physical, life, and social sciences. This has been corrected in the online issue.

No abstract available.

In a dynamic, multiagent environment, an automated intelligent agent is often faced with the possibility that other agents may instigate events that hinder or help the achievement of its own goals. To act intelligently in such an environment, an automated agent needs an event tracking capability to continually monitor the occurrence of such events and the temporal relationships among them. This capability enables an agent to infer the occurrence of important unobserved events as well as to obtain a better understanding of the interaction among events. This article focuses on event tracking in one complex and dynamic multiagent environment: the air‐combat simulation environment. It analyzes the challenges that an automated pilot agent must face when tracking events in this environment. This analysis reveals three new issues that have not been addressed in previous work in this area: (i) tracking events generated by agents’ flexible and reactive behaviors, (ii) tracking events in the context of continuous agent interactions, and (iii) tracking events in real time. This article proposes one solution to address these issues. One key idea in this solution is that the (architectural) mechanisms that an agent employs in generating its own flexible and reactive behaviors can be used to track other agents’ flexible and reactive behaviors in real time. A second key idea is the use of a world‐centered representation for modeling agent interactions. The solution is demonstrated using an implementation of an automated pilot agent.

A specialized match algorithm called Uni-Rete is reported. Uni-Rete is a specialization of the widely used Rete match algorithm to unique attributes. Performance results for Uni-Rete indicate over tenfold speedup with respect to Rete. The implications of Uni-Rete for non-unique-attribute systems are discussed.>

This paper presents an experiment in knowledge-intensive programming within a general problem-solving production-system architecture called Soar. In Soar, knowledge is encoded within a set of problem spaces, which yields a system capable of reasoning from first principles. Expertise consists of additional rules that guide complex problem-space searches and substitute for expensive problem-space operators. The resulting system uses both knowledge and search when relevant. Expertise knowledge is acquired either by having it programmed, or by a chunking mechanism that automatically learns new rules reflecting the results implicit in the knowledge of the problem spaces. The approach is demonstrated on the computer-system configuration task, the task performed by the expert system R1.