Research Lines | Ganesha

Formal Network Language and Cognitive Network Surrogates

This research line focuses on the design of a unified formal semantic language and on the development of cognitive network surrogates enabling query-driven network behavior. The objective is to provide the network with structured representations of contextual information, events, constraints, and relationships, together with mechanisms to interpret user queries.

Natural language queries are translated into predicate-based representations that coordinate sensing, knowledge retrieval, and reasoning processes across the distributed infrastructure. This enables the network to operate on explicit semantic abstractions rather than on raw data or predefined workflows.

The cognitive network surrogate acts as a reasoning model of the system and its environment, supporting the reconstruction of relationships among events and enabling the network to evaluate context before acting.

"Enabling query-driven semantic representations for context-aware network reasoning."

Deductive Reasoning over Network Surrogates

Building on the formal semantic language and cognitive network surrogate developed in RL1, this research line focuses on embedding deductive reasoning mechanisms directly within the network.

The objective is to enable the network to perform multi-step, logic-driven inference over events, states, and actions, supporting causal and counterfactual reasoning across time, space, and domains. This allows the system not only to identify correlations, but to interpret why certain outcomes occur and how alternative conditions or decisions would affect them.

Through this capability, the network moves from reactive, pattern-based behavior to structured reasoning, enabling more reliable and context-aware decision support.

"Enabling multi-step, causal, and counterfactual reasoning directly within the network."

Deductive–Inductive Integration and Human-Interrogable Networks

This research line focuses on the integration of deductive reasoning with inductive learning, enabling the network to combine structured inference with data-driven adaptation.

Inductive models learn from observations and streaming data, while deductive reasoning constrains and guides the learning process, ensuring consistency, interpretability, and robustness. This integration allows the network to continuously refine its understanding of the environment as new information becomes available.

A key objective is to make the network directly interrogable by human users. Natural language queries trigger not only reasoning, but also adaptive information gathering, enabling the system to collect additional data from the environment and incorporate user-specific context when needed.

Through this interaction, the network evolves from a passive service platform into an active system capable of supporting contextual and personalized decision processes.

"Enabling networks that learn from data while reasoning over context and interacting directly with users."

End-to-End Theoretical Foundations and Performance Characterization

This research line focuses on developing a rigorous theoretical framework for the end-to-end analysis of the GANESHA system.

The objective is to characterize the behavior of query-driven sensing and reasoning processes in terms of correctness, stability, robustness, and convergence, while capturing the trade-offs between latency, accuracy, scalability, and resource consumption. Beyond traditional network metrics, this line introduces new measures to evaluate reasoning quality, explainability, and reliability under uncertainty.

By providing formal guarantees and quantitative evaluation tools, this research line ensures that the proposed framework is not only conceptually innovative, but also analytically grounded and measurable.

"Providing formal guarantees and quantitative metrics for the Network-fo-Humans paradigm."