The State University of New York at Buffalo's Stuart C. Shapiro and colleagues developed SNePS in the late 1970s; since then, it has evolved into an essential tool for artificial intelligence studies.

Graph-like structure

SNePS utilizes a semantic network model, a graph-like structure with nodes representing concepts or objects and edges indicating relationships or associations between them. SNePS adopted a network-based method instead of hierarchical structures or predicate logic used in previous AI systems, allowing for versatile representation of intricate knowledge and facilitating advanced reasoning abilities.

Frame-based system

In addition to being a logic-based system, SNePS is both a frame-based and network-based KRRA system. Because an SNePS knowledge base (KB) comprises a collection of assertions (propositions) regarding different entities, it employs an assertional knowledge model. Its proposed model is an intensional domain of mental entities, i.e., the propositions an agent believes and the entities it conceives. The lack of an inherent equality operator allows intentionality because two syntactically distinct concepts could have marginally different Fregean meanings.

Inference methods

Three different inference methods are available in SNePS: formula-based, which comes from its logic-based personality; slot-based, which comes from its frame-based personality; and path-based, which comes from its network-based personality. All three, nevertheless, are interconnected and work in tandem.

It is possible to utilize SNePS as a standalone KRR system. In line with the GLAIR agent architecture (a layered cognitive architecture), it has also been used with its integrated acting component to create the mind of intelligent agents (cognitive robots). SNePS Research Group refers to their agents as Cassie a lot.

Network-based system

The initial definition of "SNePS" was "The Semantic Network Processing System" since it is a propositional semantic network and is a network-based system. Each slot is represented as a directed labelled arc connecting each filler node to every frame node, and each constant and functional term is a network node. According to the intended interpretation, each node represents a mental entity, some of which are propositions, and each node that implies a proposition in the network represents a proposition. Specific nodes are variables that span over and only over nodes in the SNePS logic.

Context-dependent information

SNePS stands out for its capacity to manage context-dependent information efficiently. SNePS can handle many viewpoints, attitudes, and assumptions by organizing knowledge into contexts, enabling sophisticated reasoning and decision-making. SNePS can represent human-like cognition more precisely by being context-sensitive, reflecting the dynamic nature of knowledge and its interpretation in different settings.

Reasoning processes

SNePS incorporates inference methods to aid in reasoning processes. Utilizing inheritance, deduction, and abduction techniques can generate fresh insights from current data, deduce causality, forecast outcomes, and clarify uncertainties. Due to its inferential power, SNePS can handle complicated problems in various fields, such as natural language processing, expert systems, and cognitive modelling.

Intelligent systems

SNePS highlights the significance of action in intelligent systems, together with knowledge representation and reasoning. It includes systems for planning and execution, allowing autonomous agents to turn logical conclusions into suitable behaviours. SNePS goes beyond theoretical abstraction by connecting cognition and behaviour, showing actual usefulness in real-world applications, including robotics, intelligent agents, and decision support systems.

Conclusion

SNePS has spurred several developments in AI research and shaped the evolution of other knowledge representation systems throughout the years. Its impact goes beyond academia, being used in industry and influencing the fields of cognitive science and computational intelligence.

SNePS exemplifies the ongoing pursuit of advanced AI systems that can comprehend, deduce, and operate in intricate settings. SNePS has significantly impacted the field of artificial intelligence through its new methods of knowledge representation, reasoning, and action, advancing the development of genuinely intelligent machines.

Image source: Unsplash