64 4 Brief Survey of Cognitive Architectures
and then distributing the system’s resources accordingly, based on a probabilistic algorithm. (It’s interesting to note that while NARS uses probability theory as part of its control mecha- nism, the logic it uses to represent its own knowledge about the world is nonprobabilistic. This is considered conceptually consistent, in the context of NARS theory, because system control is viewed as a domain where the system’s knowledge is more complete, thus more amenable to probabilistic reasoning.)
4.2.5 GLAIR and SNePS
Another logic-focused cognitive architecture, very different from NARS in detail, is Stuart Shapiro’s GLAIR cognitive architecture, which is centered on the SNePS paraconsistent logic [SEO7].
Like NARS, the core “cognitive loop” of GLAIR is based on reasoning: either thinking about some percept (e.g. linguistic input, or sense data from the virtual or physical world), or answer- ing some question. This inference based cognition process is turned into an intelligent agent control process via coupling it with an acting component, which operates according to a set of policies, each one of which tells the system when to take certain internal or external actions (including internal reasoning actions) in response to its observed internal and external situation.
GLAIR contains multiple layers:
e the Knowledge Layer (KL), which contains the beliefs of the agent, and is where reasoning, planning, and act selection are performed
e the Sensori-Actuator Layer (SAL), contains the controllers of the sensors and effectors of the hardware or software robot.
e the Perceptuo-Motor Layer (PML), which grounds the KL symbols in perceptual structures and subconscious actions, contains various registers for providing the agent’s sense of situ- atedness in the environment, and handles translation and communication between the KL and the SAL.
The logical Knowledge Layer incorporates multiple memory types using a common represen- tation (including declarative, procedural, episodic, attentional and intentional knowledge, and meta-knowledge). To support this broad range of knowledge types, a broad range of logical in- ference mechanisms are used, so that the KL may be variously viewed as predicate logic based, frame based, semantic network based, or from other perspectives.
What makes GLAIR more robust than most logic based AI approaches is the novel para- consistent logical formalism used in the knowledge base, which means (among other things) that uncertain, speculative or erroneous knowledge may exist in the system’s memory without leading the system to create a broadly erroneous view of the world or carry out egregiously unintelligent actions. CogPrime is not thoroughly logic-focused like GLAIR is, but in its logical aspect it seeks a similar robustness through its use of PLN logic, which embodies properties related to paraconsistency.
Compared to CogPrime, we see that GLATR. has a similarly integrative approach, but that the integration of different sorts of cognition is done more strictly within the framework of logical knowledge representation.
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