Analogy and Qualitative Representations in the Companion Cognitive Architecture

By Forbus, Kenneth D.; Hinrichs, Thomas | AI Magazine, Winter 2017 | Go to article overview

Analogy and Qualitative Representations in the Companion Cognitive Architecture


Forbus, Kenneth D., Hinrichs, Thomas, AI Magazine


Every cognitive architecture starts with a set of theoretical commitments. We have argued (Forbus 2016) that human-level artificial intelligences will be built by creating sufficiently smart software social organisms. By that we mean systems capable of interacting with people using natural modalities, operating and learning over extended periods of time, as apprentices and collaborators, instead of as tools. Just as we cannot directly access the internal representations of the people and animals we work with, cognitive systems should be able to work with us on our terms. But how does one create such systems? We have two core hypotheses, inspired by research in cognitive science:

Our first core hypothesis is that analogical reasoning and learning are central to human cognition. There is evidence that processes described by Gentner's (1983) structure-mapping theory of analogy and similarity operate throughout human cognition, including visual perception (Sagi, Gentner, and Lovett 2012), reasoning and decision making (Markman and Medin 2002), and conceptual change (Gentner et al. 1997).

Our second core hypothesis is that qualitative representations (QRs) are a key building block of human conceptual structure. Continuous phenomena and systems permeate our environment and our ways of thinking about it. This includes the physical world, where qualitative representations have a long track record of providing human-level reasoning and performance (Forbus 2014), but also in social reasoning (for example, degrees of blame [Tomai and Forbus 2007]). Qualitative representations carve up continuous phenomena into symbolic descriptions that serve as a bridge between perception and cognition, facilitate everyday reasoning and communication, and help ground expert reasoning.

The focus of the Companion cognitive architecture (Forbus, Klenk, and Hinrichs 2009) is on higher-order cognition: conceptual reasoning and learning, and learning through interactions with others, by contrast with architectures that have focused on skill learning (for example, ACT-R [Anderson 2009] and SOAR [Laird 2012]). In Newell's (1990) timescale decomposition of cognitive phenomena, conceptual reasoning and learning occur in what are called the rational and social bands,1 unlike many architectures, which start with Newell's cognitive band (Laird, Lebiere, and Rosenbloom 2017). Thus we approximate subsystems whose operations occur at faster time scales, using methods whose outputs have reasonable cognitive fidelity, although we are not making theoretical claims about them. For example, in Companions constraint checking and simple inferences are carried out through a logicbased truth-maintenance system (Forbus and de Kleer 1994). Similarly, the Companion architecture is implemented as a multiagent system, capable of running on a single laptop or distributed across many cluster nodes, depending on the task. We suspect that many data-parallel operations and a number of coarse-grained parallel operations are important for creating robust software organisms, but this current organization is motivated more by engineering concerns. By contrast, we spend considerable effort on sketch understanding and natural language understanding, since interaction through natural modalities is a core concern.

Cognitive systems are knowledge rich (for example, McShane [2017]). The Companion architecture uses the Cyc2 ontology and knowledge base (KB) contents, plus our own extensions including additional linguistic resources, representations for visual/spatial reasoning, and the hierarchical task network (HTN) plans that drive Companion operations. This is in sharp distinction with current practice in machine learning, where the goal is that systems for every task must be learned from scratch. The machine-learning (ML) approach can be fine for specific applications, but it means that many independently learnable factors in a complex task must be learned at the same time. …

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