will not apply. Although present technology makes it possible to add a huge number of productions to simulations to handle generalization and encounters with new situations, this is not the most elegant or efficient solution. Systems with similarity-based generalization and alternative learning approaches are worth considering for this reason. Some artificial intelligence research with regard to learning in continuous environments has demonstrated proof-of-concept models in which Soar learns appropriately (Modi et al., 1990; Rogers, 1996). However, it is uncertain whether these techniques are veridical models of human learning in continuous domains.
An alternative to the Soar model of learning is ACT-R, a rule-based learning model (see Chapter 3). During the past 25 years, Anderson (1993) and colleagues have been developing an alternative architecture of cognition. During this time they also have been systematically collecting empirical support for this theory across a variety of applications, including human memory, category learning, skill learning, and problem solving. There are many similarities between ACT-R and Soar. For example, some versions of ACT-R include a chunking process for learning solutions to problems. But there are also some important differences.
First, ACT-R assumes two different types knowledge—declarative knowledge, representing facts and their semantic relations (not explicit in Soar), and production rules, used for problem solving (as in Soar). Anderson and Lebiere (1998) have accumulated strong empirical evidence from human memory research, including fan effects and interference effects, to support his assumptions regarding declarative memory.
A second important feature of ACT-R concerns the principles for selecting productions. Associated with each production rule is an estimate of its expected gain and cost, and a production rule is selected probabilistically as a function of the difference between the two. The expected gain and cost are estimated by a Bayesian updating or learning model, which provides adaptive estimates for the values of each rule.
A third important feature of ACT-R concerns the principles for activating a production rule. A pattern-matching process is used to match the current situation to the conditions of a rule, and an associative retrieval process is used to retrieve information probabilistically from long-term memory. The retrieval process also includes learning mechanisms that change the associative strengths based on experience.
One drawback of ACT-R is that it is a relatively complex system involving numerous detailed assumptions that are important for its successful operation. Finally, it is necessary to compare the performance of ACT-R with that of some simpler alternatives presented below.
Some of the most advanced military simulation models employ the Soar architecture, but without the use of the chunking learning option. The simplest way to incorporate learning into these simulations would be to activate this option. In the long run, it may prove useful to explore the use of ACT-R in such