AI Magazine Volume 27 Number 2 (2006) (© AAAI) Articles Cognitive Architectures and General Intelligent Systems Pat Langley Unfortunately, modern artificial intelligence ■ In this article, I claim that research on cognitive ar- has largely abandoned this objective, having chitectures is an important path to the develop- ment of general intelligent systems. I contrast this instead divided into many distinct subfields paradigm with other approaches to constructing that care little about generality, intelligence, or such systems, and I review the theoretical commit- even systems. Subfields like computational lin- ments associated with a cognitive architecture. I il- guistics, planning, and computer vision focus lustrate these ideas using a particular architec- their attention on specific components that ture—ICARUS—by examining its claims about underlie intelligent behavior, but seldom show memories, about the representation and organiza- concern about how they might interact with tion of knowledge, and about the performance and each other. Subfields like knowledge represen- learning mechanisms that affect memory struc- tation and machine learning focus on idealized tures. I also consider the high-level programming language that embodies these commitments, draw- tasks like inheritance, classification, and reac- ing examples from the domain of in-city driving. tive control that ignore the richness and com- In closing, I consider ICARUS’s relation to other cog- plexity of human intelligence. nitive architectures and discuss some open issues The fragmentation of artificial intelligence that deserve increased attention. has taken energy away from efforts on general intelligent systems, but it has led to certain types of progress within each of its subfields. Despite this subdivision into distinct commu- nities, the past decade has seen many applica- The Need for General tions of AI technology developed and fielded Intelligent Systems successfully. Yet these systems have a “niche” flavor that differs markedly from those origi- he original goal of artificial intelligence nally envisioned by the field’s early researchers. was the design and construction of com- More broadly based applications, such as hu- putational artifacts that combined many T man-level tutoring systems, flexible and in- cognitive abilities in an integrated system. structable household robots, and believable These entities were intended to have the same characters for interactive entertainment, will intellectual capacity as humans and they were require that we develop truly integrated intelli- supposed to exhibit their intelligence in a gen- gent systems rather than continuing to focus eral way across many different domains. I will on isolated components. refer to this research agenda as aimed at the cre- As Newell (1973) argued, “You can’t play ation of general intelligent systems. Copyright © 2006, American Association for Artificial Intelligence. All rights reserved. 0738-4602-2006 / $2.00 SUMMER 2006 33 Articles twenty questions with nature and win.” At the However, Newell’s vision for research on in- time, he was critiquing the strategy of experi- tegrated theories of intelligence included more mental cognitive psychologists, who studied than either of these frameworks provides. He isolated components of human cognition with- believed that agent architectures should incor- out considering their interaction. However, porate strong theoretical assumptions about over the past decade, his statement has become the nature of the mind. An architectural design an equally valid criticism of the fragmented na- should change only gradually, as one deter- ture of AI research. Newell proposed that we mines that new structures and processes are re- move beyond separate phenomena and capa- quired to support new functionality. Moreover, bilities to develop complete models of intelli- early design choices should constrain heavily gent behavior. Moreover, he believed that we those made later, producing far more interde- should demonstrate our systems’ intelligence pendence among modules than assumed by ei- on the same range of domains and tasks as han- ther multiagent or blackboard systems. Newell dled by humans, and that we should evaluate (1990) claimed that architectural research is all them in terms of generality and flexibility, about mutual constraints, and its aim should rather than success on a single domain. He also be a unified theory of intelligent behavior, not viewed artificial intelligence and cognitive psy- merely an integrated one. chology as close allies with distinct yet related The notion of a cognitive architecture revolves goals that could benefit greatly from working around this interdependent approach to agent together. This proposal was linked closely to his design. Following Newell’s lead, research on notion of a cognitive architecture, an idea that I such architectures makes commitments about: can best explain by contrasting it with alterna- (1) the short-term and long-term memories tive frameworks. that store the agent’s beliefs, goals, and knowl- edge; (2) the representation and organization of structures that are embedded in these mem- Three Architectural Paradigms ories; (3) the functional processes that operate Artificial intelligence has explored three main on these structures, including both perfor- avenues to the creation of general intelligent mance and learning mechanisms; and (4) a systems. Perhaps the most widely known is the programming language that lets one construct multi-agent systems framework (Sycara 1998), knowledge-based systems that embody the ar- which has much in common with traditional chitecture’s assumptions. These commitments approaches to software engineering. In this provide much stronger constraints on the con- scheme, one develops distinct modules for dif- struction of intelligent agents than do alterna- ferent facets of an intelligent system, which tive frameworks, and they constitute a compu- then communicate directly with each other. tational theory of intelligence that goes beyond The architecture specifies the inputs/outputs of providing a convenient programming para- each module and the protocols for communi- digm. cating among them, but places no constraints In the next section, I will use one such cog- on how each component operates. Indeed, the nitive architecture—ICARUS—to illustrate each ability to replace one large-scale module with of these commitments in turn. ICARUS is neither another equivalent one is viewed as an advan- the oldest nor the most developed architecture; tage of this approach, since it lets teams devel- some frameworks, like ACT (Anderson 1993) op them separately and eases their integration. and Soar (Laird, Newell, and Rosenbloom One disadvantage of the multi-agent systems 1987), have undergone continual development framework is the need for modules to commu- for more than two decades. However, it will nicate directly with one another. Another par- serve well enough to make the main points, adigm addresses this issue by having modules and its differences from more traditional cogni- read and alter a shared memory of beliefs, tive architectures will clarify the breadth and goals, and other short-term structures. Such a diversity of this approach to understanding the blackboard system (Engelmore and Morgan nature of intelligence. 1989) retains the modularity of the first frame- In discussing ICARUS, I will draw examples work, but replaces direct communication from the domain of in-city driving, for which among modules with an indirect scheme that we have implemented a simulated environ- relies on matching patterns against elements in ment that simplifies many aspects but remains the short-term memory. Thus, a blackboard ar- rich and challenging (Choi et al. 2004). Objects chitecture supports a different form of integra- in this environment include vehicles, for tion than the multiagent scheme, so that the which the positions, orientations, and veloci- former comes somewhat closer to theories of ties change over time, as well as static objects human cognition. like road segments, intersections, lane lines, 34 AI MAGAZINE Articles Perceptual Buffer Conceptual Belief Memory Memory Long-Term Short-Term Environment Memories Memories Skill Goal/Intention Memory Memory Motor Buffer Figure 1. Six Long-Term and Short-Term Memories of the ICARUS Architecture. sidewalks, and buildings. Each vehicle can alter general intelligent systems: (1) cognition is its velocity and change its steering wheel angle grounded in perception and action; (2) con- by setting control variables, which interact cepts and skills are distinct cognitive structures; with realistic laws to determine each vehicle’s (3) long-term memory is organized in a hierar- state. We have implemented ICARUS agents in chical fashion; (4) skill and concept hierarchies other domains, but this is the most complex are acquired in a cumulative manner; and (5) and will serve best to communicate my main long-term and short-term structures have a points. strong correspondence. These ideas further dis- tinguish ICARUS from most other cognitive ar- chitectures that have been developed within CARUS The I Architecture the Newell tradition. Again, I will not claim As noted above, ICARUS is a cognitive architec- here that they make the framework superior to ture in Newell’s sense of that phrase. Like its earlier ones, but I believe they do clarify the di- predecessors, it makes strong commitments to mensions that define the space of candidate ar- memories, representations,