Soar is a cognitive architecture, originally created by John Laird, Allen Newell, and Paul Rosenbloom at Carnegie Mellon University. The goal of the Soar project is to develop the fixed computational building blocks necessary for general intelligent agents – agents that can perform a wide range of tasks and encode, use, and learn all types of knowledge to realize the full range of cognitive capabilities found in humans, such as decision making, problem solving, planning, and natural-language understanding. It is both a theory of what cognition is and a computational implementation of that theory. Since its beginnings in 1983 as John Laird’s thesis, it has been widely used by AI researchers to create intelligent agents and cognitive models of different aspects of human behavior. The most current and comprehensive description of Soar is the 2012 book, The Soar Cognitive Architecture. Rosenbloom continued to serve as co-principal investigator after moving to Stanford University, then to the University of Southern California's Information Sciences Institute. It is now maintained and developed by John Laird's research group at the University of Michigan.
Theory Soar embodies multiple hypotheses about the computational structures underlying general intelligence, many of which are shared with other cognitive architectures, including ACT-R, which was created by John R. Anderson, and LIDA, which was created by Stan Franklin. Recently, the emphasis on Soar has been on general AI (functionality and efficiency), whereas the emphasis on ACT-R has always been on cognitive modeling (detailed modeling of human cognition). The original theory of cognition underlying Soar is the Problem Space Hypothesis, which is described in Allen Newell's book, Unified Theories of Cognition. and dates back to one of the first AI systems created, Newell, Simon, and Shaw's Logic Theorist, first presented in 1955 and as the General Problem Solver in 1957. The Problem Space Hypothesis contends that all goal-oriented behavior can be cast as search through a space of possible states (a problem space) while attempting to achieve a goal. At each step, a single operator is selected, and then applied to the agent’s current state, which can lead to internal changes, such as retrieval of knowledge from long-term memory or modifications or external actions in the world. (Soar’s name is derived from this basic cycle of State, Operator, And Result; however, it is no longer regarded as an acronym.) Inherent to the Problem Space Hypothesis is that all behavior, even a complex activity such as planning, is decomposable into a sequence of selection and application of primitive operators, which when mapped onto human behavior take ~50ms. A second hypothesis of Soar’s theory is that although only a single operator can be selected at each step, forcing a serial bottleneck, the processes of selection and application are implemented through parallel rule firings, which provide context-dependent retrieval of procedural knowledge. A third hypothesis is that if the knowledge to select or apply an operator is incomplete or uncertain, an impasse arises and the architecture automatically creates a substate. In the substate, the same process of problem solving is recursively used, but with the goal to retrieve or discover knowledge so that decision making can continue. This can lead to a stack of substates, where traditional problem methods, such as planning or hierarchical task decomposition, naturally arise. When results created in the substate resolve the impasse, the substate and its associated structures are removed. The overall approach is called Universal Subgoaling. These assumptions lead to an architecture that supports three levels of processing. At the lowest level, is bottom-up, parallel, and automatic processing. The next level is the deliberative level, where knowledge from the first level is used to propose, select, and apply a single action. These two levels implement fast, skilled behavior, and roughly correspond to Kahneman’s System 1 processing level. More complex behavior arises automatically when knowledge is incomplete or uncertain, through a third level of processing using substates, roughly corresponding to System 2. A fourth hypothesis in Soar is that the underlying structure is modular, but not in terms of task or capability based modules, such as planning or language, but instead as task independent modules including: a decision making module; memory modules (short-term spatial/visual and working memories; long-term procedural, declarative, and episodic memories), learning mechanisms associated with all long-term memories; and perceptual and motor modules. There are further assumptions about the specific properties of these memories described below, including that all learning is online and incremental. A fifth hypothesis is that memory elements (except those in the spatial/visual memory) are represented as symbolic, relational structures. The hypothesis that a symbolic system is necessary for general intelligence is known as the physical symbol system hypothesis. An important evolution in Soar is that all symbolic structures have associated statistical metadata (such as information on recency and frequency of use, or expected future reward) that influences retrieval, maintenance, and learning of the symbolic structures.
Architecture
… excerpt ends here. Continue reading the full article.
