A Cognitive Science Based Machine Learning Architecture

In an attempt to illustrate the application of cognitive science principles to hard AI problems in machine learning we propose the LIDA technology, a cognitive science based architecture capable of more human-like learning. A LIDA based software agent or cognitive robot will be capable of three fundamental, continuously active, humanlike learning mechanisms: 1) perceptual learning, the learning of new objects, categories, relations, etc., 2) episodic learning of events, the what, where, and when, 3) procedural learning, the learning of new actions and action sequences with which to accomplish new tasks. The paper argues for the use of modular components, each specializing in implementing individual facets of human and animal cognition, as a viable approach towards achieving general intelligence. Relevance of Cognitive Science to AI Dating back to Samuel’s checker player (1959), machine learning is among the oldest of the sub-branches of AI with many practitioners and many successes to its credit. Still, after fifty years of effort there are remaining difficulties. Machine learning often requires large, accurate training sets, shows little awareness of what’s known or not known, integrates new knowledge poorly into old, learns only one task at a time, allows little transfer of learned knowledge to new tasks, and is poor at learning from human teachers. Clearly, machine learning presents a number of hard AI problems. Can cognitive science help? In contrast, human learning has solved many of these problems, and is typically continual, quick, efficient, accurate, robust, flexible, and effortless. As an example consider perceptual learning, the learning of new objects, categories, relations, etc. Traditional machine learning approaches such as object detection, classification, clustering, etc, are highly susceptible to the problems raised above. However, perceptual learning in humans and animals seem to have no such restrictions. Perceptual learning in humans occurs incrementally so there is no need for a large training set. Learning and knowledge extraction are achieved simultaneously through a dynamical system that can adapt to changes in the nature of the stimuli perceived in the environment. Additionally, human like learning is based on reinforcement rather than fitting to a dataset or model. Therefore, in addition to learning, humans can also forget. Initially, many associations are made between entities, the ones that are sufficiently reinforced persist, while the ones that aren’t decay. All this suggests a possible heuristic: If you want smart software, copy it after humans. We’ve done just that. The Learning Intelligent Distribution Agent (LIDA) architecture that we propose here was designed to be consistent with what is known from cognitive science and neuroscience. In addition to being a computational architecture, it’s intended to model human cognition. We’ll go on to describe the LIDA architecture and its human-like learning capabilities. Modularity as an Approach for Intelligence LIDA provides a conceptual and computational model of cognition. It is the learning extension of the original IDA system implemented as a software agent. IDA ‘lives’ on a computer system with connections to the Internet and various databases, and does personnel work for the US Navy, performing all the specific personnel tasks of a human (Franklin 2001). The LIDA architecture is partly symbolic and partly connectionist with all symbols being grounded in the physical world in the sense of Brooks (1986; 1990). We argue for unique, specialized mechanisms to computationally implement the various facets of human cognition such as perception, episodic memories, functional consciousness, and action selection. We offer an evolutionary argument and a functional argument to justify the specialized, modular component approach to the design of an intelligent system. The evolutionary argument draws support from the sustained efforts by neuroscientists and brain physiologists in mapping distinct functions to different areas in the brain. In many ways the brain can be viewed as a kluge of different mechanisms. For example, parts of perceptual associative memory are believed to be in the perirhinal cortex (Davachi, Mitchell & Wagner 2003), while some of the neural correlates of autobiographical memory have been identified as the medial frontal cortex and left 1Compilation copyright © 2006, American Association for Artificial Intelligence (www.aaai.org). All rights reserved. hippocampus (Conway & Fthenaki 2000; Maguire 2001). In addition to the neuroscience evidence, there are developmental arguments for a distinct mechanism for perceptual memory. Infants who have not yet developed object permanence (any episodic memory) are quite able to recognize and categorize (Mandler 2000). Other arguments come from studies of human amnesiacs with significant loss of declarative memory, but mostly intact perceptual associative memory and learning (Gabrieli et al. 1990, Fahle & Daum 2002). Perhaps the most convincing argument comes from experiments with rats in a radial arm maze. With four arms baited and four not (with none restocked), normal rats learn to recognize which arms to search (perceptual associative memory) and remember which arms they have already fed in (episodic memory) so as not to search there a second time. Rats with their hippocampal systems excised lose their episodic memory but retain perceptual associative memory, again arguing for distinct mechanisms (Olton, Becker, & Handelman 1979). Similarly, arguments for finer distinctions between the various episodic memory systems have been made. Episodic memories are memories for events of the what, where, and when. Conway (2001) argues for a unique memory system for recent, highly specific, sensory-perceptual information, than autobiographical memory, on the basis of different functions, knowledge stored, access techniques, phenomenology, and neurology. Additionally, Moscovitch et al. (2005) offer neuroimaging evidence for a finer grained component analysis for semantic and spatial memories. The functional arguments in support of specialized modular components for general intelligence are derived from the need for primitives for any agent capable of robust autonomy. An initial set of primitive feature detectors for perception and a set of primitive effectors for action execution are a computational necessity for any autonomous agent, natural or artificial, software or robotic (Franklin 1997). Additionally, as widely recognized in humans, the need for primitive motivators implemented as feelings and emotions may also be required for any cognitive system that attempts to stimulate general intelligence. In humans, primitive feature detectors for vision include neurons in the primary visual cortex (V1) detecting line segments at various orientations, while primitive effectors include neuronal groups controlling individual muscles. Higher level visual perception such as categorization, object detection, etc, is realized from associations between the various primitive feature detectors. Similarly, more complex actions and action sequences are learnt by associating the primitive effectors. These functional requirements that differ between perception and action strengthen the argument for specialized modular components. The LIDA Architecture On the basis of the arguments for specialized, modular components as an approach for intelligence, the LIDA architecture operates by the interplay of unique mechanisms implementing the major facets of human cognition. The mechanisms used in implementing the several modules have been inspired by a number of different ‘new AI’ techniques (Drescher 1991; Hofstadter & Mitchell 1994; Jackson 1987; Kanerva 1988; Maes 1989; Brooks 1986). We now describe LIDA’s primary mechanisms. Perceptual Associative Memory. LIDA perceives both exogenously and endogenously with Barsalou’s perceptual symbol systems serving as a guide (1999). The perceptual knowledge-base of this agent, called perceptual associative memory, takes the form of a semantic net with activation called the slipnet, a la Hofstadter and Mitchell’s Copycat architecture (1994). Nodes of the slipnet constitute the agent’s perceptual symbols, representing individuals, categories, and perhaps higher-level ideas and concepts. Pieces of the slipnet containing nodes and links, together with perceptual codelets (a codelet is a small piece of code running independently; perceptual codelets are a special type of codelet designed for perceptual tasks such as recognition) with the task of copying the piece to working memory, constitute Barsalou’s perceptual symbol simulators (1999). Together they constitute an integrated perceptual system for LIDA, allowing the system to recognize, categorize and understand. Workspace. LIDA’s workspace is analogous to the preconscious buffers of human working memory. Perceptual codelets write to the workspace as do other, more internal codelets. Attention codelets (codelets that form coalitions with other codelets to compete for functional consciousness) watch what is written in the workspace in order to react to it. Items in the workspace decay over time, and may be overwritten. Another pivotal role of the workspace is the building of temporary structures over multiple cognitive cycles (see below). Perceptual symbols from the slipnet are assimilated into existing relational and situational templates while preserving spatial and temporal relations between the symbols. The structures in the workspace also decay rapidly. Episodic Memory. Episodic memory in the LIDA architecture is composed of a declarative memory (DM) for the long term storage of autobiographical and semantic information as well as a short term transient episodic memory (TEM) similar to Conway’s (2001) sensoryperceptual episodic memory with a retention rate measured in hours. LIDA employs variants of sparse distributed memory