We start by making a distinction between mind and cognition, and by positing that cognition is an aspect of mind. We propose as a working hypothesis a Separability Hypothesis which posits that we can factor off an architecture for cognition from a more general architecture for mind, thus avoiding a number of philosophical objections that have been raised about the "Strong AI" hypothesis. Thus the search for an architectural level which will explain all the interesting phenomena of cognition is likely to be futile. There are a number of levels which interact, unlike in the computer model, and this interaction makes explanation of even relatively simple cognitive phenomena in terms of one level quite incomplete.
I. Dimensions for Thinking About Thinking:
A major problem in the study of intelligence and cognition is the range of—often implicit—assumptions about what phenomena these terms are meant to cover. Are we just talking about cognition as having and using knowledge, or are we also talking about other mental states such as emotions and subjective awareness? Are we talking about intelligence as an abstract set of capacities, or as a set of biological mechanisms and phenomena? These two questions set up two dimensions of discussion about intelligence. After we discuss these dimensions we will discuss information processing, representation, and cognitive architectures.
A. Dimension 1: Is intelligence separable from other mental phenomena?
When people think of intelligence and cognition, they often think of an agent being in some knowledge state, that is, having thoughts, beliefs. They also think of the underlying process of cognition as something that changes knowledge states. Since knowledge states are particular types of information states the underlying process is thought of as information processing. However, besides these knowledge states, mental phenomena also include such things as emotional states and subjective consciousness. Under what conditions can these other mental properties also be attributed to artifacts to which we attribute knowledge states? Is intelligence separable from these other mental phenomena?
It is possible that intelligence can be explained or simulated without necessarily explaining or simulating other aspects of mind. A somewhat formal way of putting this Separability Hypothesis is that the knowledge state transformation account can be factored off as a homomorphism of the mental process account. That is: If the mental process can be seen as a sequence of transformations: M1 -->M2 -->..., where Mi is the complete mental state, and the transformation function (the function that is responsible for state changes) is F, then a subprocess K1 --> K2 -->. . . can be identified such that each Ki is a knowledge state and a component of the corresponding Mi, the transformation function is f, and f is some kind of homomorphism of F. A study of intelligence alone can restrict itself to a characterization of K’s and f, without producing accounts of M’s and F. If cognition is in fact separable in this sense, we can in principle design machines that implement f and whose states are interpretable as K’s. We can call such machines cognitive agents, and attribute intelligence to them. However, the states of such machines are not necessarily interpretable as complete M’s, and thus they may be denied other attributes of mental states.
B. Dimension 2: Functional versus Biological:
The second dimension in discussions about intelligence involves the extent to which we need to be tied to biology for understanding intelligence. Can intelligence be characterized abstractly as a functional capability which just happens to be realized more or less well by some biological organisms? If it can, then study of biological brains, of human psychology, or of the phenomenology of human consciousness is not logically necessary for a theory of cognition and intelligence, just as enquiries into the relevant capabilities of biological organisms are not needed for the abstract study of logic and arithmetic or for the theory of flight. Of course, we may learn something from biology about how to practically implement intelligent systems, but we may feel quite free to substitute non-biological (both in the sense of architectures which are not brain-like and in the sense of being un- constrained by considerations of human psychology) approaches for all or part of our implementation. We might have different constraints on a definition that needed to include emotion and subjective states than one that only included knowledge states. Clearly, the enterprise of AI deeply depends upon this functional view being true at some level, but whether that level is abstract logical representations as in some branches of AI, Darwinian neural group selections as proposed by Edelman, something intermediate, or something physicalist is still an open question.
III. Architectures for Intelligence:
We now move to a discussion of architectural proposals within the information processing perspective. Our goal is to try to place the multiplicity of proposals into perspective. As we review various proposals, we will present some judgements of our own about relevant issues. But first, we need to review the notion of an architecture and make some additional distinctions.
A. Form and Content Issues in Architectures :
In computer science, a programming language corresponds to a virtual architecture. A specific program in that language describes a particular (virtual) machine, which then responds to various inputs in ways defined by the program. The architecture is thus what Newell calls the fixed structure of the information processor that is being analyzed, and the program specifies a variable structure within this architecture. We can regard the architecture as the form and the program as the content, which together fully instantiate a particular information processing machine. We can extend these intuitions to types of machines which are different from computers. A particular connectionist machine is then instantiated by the "program" that specifies values for all these variables.
We have discussed the prospects for separating intelligence (a knowledge state process) from other mental phenomena, and also the degree to which various theories of intelligence and cognition balance between fidelity to biology versus functionalism. We have discussed the sense in which alternatives such as logic, decision tree algorithms, and connectionism are all alternative languages in which to couch an information processing account of cognitive phenomena, and what it means to take a Knowledge Level stance towards cognitive phenomena. We have further discussed the distinction between form and content theories in AI.
B. Intelligence as Just Computation:
Until recently the dominant paradigm for thinking about information processing has been the Turing machine framework, or what has been called the discrete symbol system approach. Information processing theories are formulated as algorithms operating on data structures. In fact AI was launched as a field when Turing proposed in a famous paper that thinking was computation of this type (the term "artificial intelligence" itself was coined later) . Natural questions in this framework would be whether the set of computations that underlie thinking is a subset of Turing-computable functions, and if so how the properties of the subset should be characterized.
Most of AI research consists of algorithms for specific problems that are associated with intelligence when humans perform them. Algorithms for diagnosis, design, planning, etc., are proposed, because these tasks are seen as important for an intelligent agent. But as a rule no effort is made to relate the algorithm for the specific task to a general architecture for intelligence. While such algorithms are useful as technologies and to make the point that several tasks that appear to require intelligence can be done by certain classes of machines, they do not give much insight into intelligence in general.
C. Architectures for Deliberation:
Historically most of the intuitions in AI about intelligence have come from introspections about the relationships between conscious thoughts. We are aware of having thoughts which often follow one after another. These thoughts are mostly couched in the medium of natural language, although sometimes thoughts include mental images as well. When people are thinking for a purpose, say for problem solving, there is a sense of directing thoughts, choosing some, rejecting others, and focusing them towards the goal. Activity of this type has been called "deliberation." Deliberation, for humans, is a coherent goal-directed activity, lasting over several seconds or longer. For many people thinking is the act of deliberating in this sense. We can contrast activities in this time span with other cognitive phenomena, which, in humans, take under a few hundred milliseconds, such as real-time natural language understanding and generation, visual perception, being reminded of things, and so on. These short time span phenomena are handled by what we will call the subdeliberative architecture, as we will discuss later.
Researchers have proposed different kinds of deliberative architectures, depending upon which kind of pattern among conscious thoughts struck them. Two groups of proposals about such patterns have been influential in AI theory-making: the reasoning view and the goal-subgoal view.
1. Deliberation as Reasoning:
People have for a long time been struck by logical relations between thoughts and have made the distinction between rational and irrational thoughts. Remember that Boole’s book on logic was titled "Laws of Thought." Thoughts often have a logical relation between them: we think thoughts A and B, then thought C, where C follows from A and B. In AI, this view has given rise to an idealization of intelligence as rational thought, and consequently to the view that the appropriate architecture is one whose behavior is governed by rules of logic. In AI, McCarthy is mostly closely identified with the logic approach to AI, and [McCarthy and Hayes, 1969] is considered a clear early statement of some of the issues in the use of logic for building an intelligent machine.
Researchers in AI disagree about how to make machines which display this kind of rationality. One group proposes that the ideal thought machine is a logic machine, one whose architecture has logical rules of inference as its primitive operators. These operators work on a storehouse of knowledge represented in a logical formalism and generate additional thoughts. For example, the Japanese Fifth generation project came up with computer architectures whose performance was measured in (millions of) inferences per second.
Historically rationality was characterized by the rules of deduction, but in AI, the notion is being broadened to include a host of non-deductive rules under the broad umbrella of "non-monotonic logic" [McCarthy, 1980] or "default reasoning," to capture various plausible reasoning rules. There is considerable difference of opinion about whether such rules exist in a domain-independent way as in the case of deduction, and how large a set of rules would be required to capture all plausible reasoning behaviors. If the number of rules is very large, or if they are context-dependent in complicated ways, then logic architectures would become less practical.
2. Deliberation as Goal-Subgoaling:
An alternate view of deliberation is inspired by another perceived relation between thoughts and provides a basic mechanism for control as part of the architecture. Thoughts are often linked by means of a goal-subgoal relation. For example, you may have a thought about wanting to go to New Delhi, then you find yourself having thoughts about taking trains and airplanes, and about which is better, then you might think of making reservations and so on. Newell and Simon  have argued that this relation between thoughts, the fact that goal thoughts spawn subgoal thoughts recursively until the subgoals are solved and eventually the goals are solved, is the essence of the mechanism of intelligence. More than one subgoal may be spawned, and so backtracking from subgoals that didn’t work out is generally necessary. Deliberation thus looks like search in a problem space. Setting up the alternatives and exploring them is made possible by the knowledge that the agent has. A long term memory is generally proposed which holds the knowledge and from which knowledge relevant to a goal is brought to play during deliberation. This analysis suggests an architecture for deliberation that retrieves relevant knowledge, sets up a set of alternatives to explore (the problem space), explores it, sets up subgoals, etc.
The most recent version of an architecture for deliberation in the goal-subgoal framework is Soar [Newell, 1990]. Soar has two important attributes. The first is that any difficulty it has in solving any subgoal simply results in the setting up of another subgoal, and knowledge from long term memory is brought to bear in its solution. It might be remembered that Newell’s definition of intelligence is the ability to realize the knowledge level potential of an agent. Deliberation and goal-subgoaling are intended to capture that capability: any piece of knowledge in long term memory is available, if it is relevant, for any goal. Repeated subgoaling will bring that knowledge to deliberation. The second attribute of Soar is that it "caches" its successes in problem solving in its long term memory. The next time there is a similar goal, that cached knowledge can be directly used, instead of searching again in the corresponding problem space.
One of the results of this form of deliberation is the construction of special purpose algorithms or methods for specific problems. These algorithms can be placed in an external computational medium, and as soon as a subgoal arises that such a method or algorithm can solve, the external medium can solve it and return the results. For example, during design, an engineer might set up the subgoal of computing the maximum stress in a truss, and invoke a finite element method running on a computer. The deliberative engine can thus create and invoke computational algorithms. The goal-subgoaling architecture provides a natural way to integrate external algorithms.
In the Soar view, long term memory is just an associative memory. It has the capability to "recognize" a situation and retrieve the relevant pieces of knowledge. Because of the learning capability of the architecture, each episode of problem solving gives rise to continuous improvement. As a problem comes along, some subtasks are solved by external computational architectures which implement special purpose algorithms, while others are directly solved by compiled knowledge in memory, while yet others are solved by additional deliberation. This cycle make the overall system increasingly more powerful.
Deliberation seems to be a source of great power in humans. Why isn’t recognition enough? As Newell points out, the particular advantage of deliberation is distal access to and combination of knowledge at run-time in a goal-specific way. In the deliberative machine, temporary connections are created between pieces of knowledge that are not hard-coded, and that gives it the ability to realize the knowledge level potential more. As an architecture for deliberation, the goal-subgoal view seems to us closer to the mark than the reasoning view. As we have argued elsewhere [Chandrasekaran, 1991], logic seems more appropriate for justification of conclusions and as the framework for the semantics of representations than for the generative architecture.
AI theories of deliberation give central importance to human-level problem solving and reasoning. Any continuity with higher animal cognition or brain structure is at the level of the recognition architecture of memory, about which this view says little other than that it is a recognition memory. For supporting deliberation at the human level, long term memory should be capable of storing and generating knowledge with the full range of ontological distinctions that human language has.
3. Is the Search View of Deliberation Too Narrow?
A criticism of this picture of deliberation as a search architecture is that it is based on a somewhat narrow view of the function of cognition. It is worth reviewing this argument briefly.
Suppose a Martian watches a human in the act of multiplying numbers. The human, during this task, is executing some multiplication algorithm, i.e., appears to be a multiplication machine. The Martian might well return to his superiors and report that the human cognitive architecture is a multiplication machine. We, however, know that the multiplication architecture is a fleeting, evanescent virtual architecture that emerged as an interaction between the goal (multiplication) and the procedural knowledge of the human. With a different goal, the human might behave like a different machine. It would be awkward to imagine cognition to be a collection of different architectures for each such task; in fact, cognition is very plastic and is able to emulate various virtual machines as needed.
If the sole purpose of the cognitive architecture is goal achievement (or "problem solving"), then it is reasonable to assume that the architecture would be hard-wired for this purpose. What, however, if goal achievement is only one of the functions of the cognitive architecture, common though it might be? At least in humans, the same architecture is used to daydream, just take in the external world and enjoy it, and so on. The search behavior that we need for problem solving can come about simply by virtue of the knowledge that is made available to the agent’s deliberation from long term memory. This knowledge is either a solution to the problem, or a set of alternatives to consider. The agent, faced with the goal and a set of alternatives, simply considers the alternatives in turn, and when additional subgoals are set, repeats the process of seeking more knowledge. In fact, this kind of search behavior happens not only with individuals, but with organizations. They too explore alternatives, but yet we don’t see a need for a fixed search engine for explaining organizational behavior. Deliberation of course has to have the right sort of properties to be able to support search. Certainly adequate working memory needs to be there, and probably there are other constraints on deliberation.
. In fact, a number of other such emergent architectures built on top of the deliberative architecture have been studied earlier in our work on Generic Task architectures . These architectures were intended to capture the needs for specific classes of goals (such as classification).The above argument is not to deemphasize the importance of problem space search for goal achievement, but to resist the identification of the architecture of the conscious processor with one exclusively intended for search. The problem space architecture is still important as the virtual architecture for goal-achieving, since it is a common, though not the only, function of cognition.
D. Subdeliberative Architectures
We have made a distinction between cognitive phenomena that take less than a few hundred milliseconds for completion and those that evolve over longer time spans. We discussed proposals for the deliberative architecture to account for phenomena taking longer time spans. Some form of subdeliberative architecture is then responsible for phenomena that occur in very short time spans in humans. In deliberation, we have access to a number of intermediate states in problem solving. Many people in AI and cognitive science feel that the emphasis on complex problem solving as the door to understanding intelligence is misplaced, and that theories that emphasize rational problem solving only account for very special cases and do not account for the general cognitive skills that are present in ordinary people. These researchers focus almost completely on the nature of the subdeliberative architecture. There is also a belief that the subdeliberative architecture is directly reflected in the structure of the neural machinery in the brain. Thus, some of the proposals for the subdeliberative architecture claim to be inspired by the structure of the brain and claim a biological basis in that sense.
The various proposals differ along a number of dimensions: what kinds of tasks the architecture performs, degree of parallelism, whether it is an information processing architecture at all, and, when it is taken to be an information processing architecture, whether it is a symbolic one or some other type.
With respect to the kind of tasks the architecture performs, we mentioned Newell’s view that it is just a recognition architecture. Any smartness it possesses is a result of good abstractions and good indexing, but architecturally, there is nothing particularly complicated. In fact, the good abstractions and indexing themselves were the result of the discoveries of deliberation during problem state search. The real solution to the problem of memory, for Newell, is to get chunking done right: the proper level of abstraction, labeling and indexing is all done at the time of chunking. In contrast to the recognition view are proposals that see relatively complex problem solving activities going on in subdeliberative cognition. Cognition in this picture is a communicating collection of modular agents, each of whom is simple, but capable of some degree of problem solving. For example, they can use the means-ends heuristic (the goal-subgoaling feature of deliberation in the Soar architecture).
V. Concluding Remarks:
We started by asking how far intelligence or cognition can be separated from mental phenomena in general. We suggested that the problem of an architecture for cognition is not really well-posed, since, depending upon what aspects of the behavior of biological agents are included in the functional specification, there can be different constraints on the architecture. We reviewed a number of issues and proposals relevant to cognitive architectures. Not only are there many levels each explaining some aspect of cognition and mentality, but the levels interact even in relatively simple cognitive phenomena.