Concurrent MetateM
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Notes and Further Reading My presentation of logic based agents draws heavily on the discussion of deliberate agents presented in Genesereth and Nilsson (1987, Chapter 13),which represents the logic-centric view of A1 and agents very well. The discussion is also partly based on Konolige (1986). A number of more-or-less 'pure' logical approaches to agent programming have been developed. Well-known examples include the ConGolog system of Lesperance and colleagues (Lesperance et al., 1996) (wbch is based on the situation calculus (McCarthy and Hayes, 1969)).Note that these architectures (and the discussion above) assume that if one adopts a logical approach to agent building, then t b s means agents are essentially theorem provers, employing explicit symbolic reasoning (theorem-proving)in order to make decisions. But just because we find logic a useful tool for conceptualizing or specifying agents, this does not mean that we must view decision making as logical manipulation. An alternative is to compile the logical specification of an agent into a form more amenable to efficient decision making. The difference is rather like the distinction between interpreted and compiled programming languages. The best-known example of this work is the situated automata paradigm of Rosenschein and Kaelbling (1996). A review of the role of logic in intelligent agents may be found in Wooldridge (1997). Finally, for a detailed discussion of calculative rationality and the way that it has affected thinking in A1 (see Russell and Subramanian, 1995). The main references to Agent0 are Shoham (1990, 1993). Shoham's AOP proposal has been enormously influential in the multiagent systems community. In addition to the reasons set out in the main text, there are other reasons for believing that an intentional stance will be useful for understanding and reasoning about computer programs (Huhns and Singh, 1998). First, and perhaps most importantly, the ability of heterogeneous, self-interested agents to communicate seems to imply the ability to talk about the beliefs, aspirations, and intentions of individual agents. For example, in order to coordinate their activities, agents must have information about the intentions of others (Jennings, 1993a).This idea is closely related to Newell's knowledge level (Newell, 1982). Later in this book, we will see how mental states such as beliefs, desires, and the like are used to give a semantics to speech acts (Searle, 1969; Cohen and Levesque, 1990a). Second, mentalistic models are a good candidate for representing information about end users. For example, imagine a tutoring system that works with students to teach them Java programming. One way to build such a system is to give it a model of the user. Beliefs, desires, intentions, and the like seem appropriate for the make-up of such models. Michael Fisher's Concurrent MetateM language is described in Fisher (1994); the execution algorithm that underpins it is described in Barringer et a[.(1989).Since Shoham's proposal, a number of languages have been proposed wbch claim to be agent oriented. Examples include Becky Thomas's Planning Communicating