Hybrid Human-AI Frameworks

• Hybrid Human-AI Frameworks are socio-technical systems that combine independent multi-agent (MAS) and integrated centaurian paradigms to balance autonomy with unified cognition.
• They utilize a three-layer communication architecture—surface, observation, and computation—to standardize sensor data, human input, and AI decisions for seamless interaction.
• These frameworks enable robust and adaptive operations by leveraging protocol-driven autonomy in MAS alongside synchronized human-AI decision-making in centaurian configurations.

Hybrid Human-AI Frameworks refer to the class of socio-technical systems in which human and artificial agents collaborate either loosely as autonomous entities (Multi-Agent Systems, MAS) or as tightly integrated, symbiotic composites (Centaurian systems), structured via multiple coordination layers and unified by formal communication-space architectures. These frameworks formalize not only the static configuration and dynamic evolution of agent roles, but also the processing and routing of heterogeneous information, ensuring both robustness through protocol-driven autonomy and synergy via real-time cognitive fusion.

1. Multi-Agent and Centaurian Paradigms

The foundational distinction in Borghoff, Bottoni, and Pareschi’s system-theoretical approach (Borghoff et al., 19 Feb 2025) is between two paradigms:

• Multi-Agent Systems (MAS): A set $A = \{a_1, ..., a_n\}$ of potentially heterogeneous autonomous agents (human and/or artificial) operating with independent local states and decision procedures. Interaction is mediated exclusively through well-defined message protocols $\Pi$ over a shared alphabet $M$, with the formal structure $A_{\text{MAS}} = (A, M, \Pi)$. MAS preserve maximal autonomy $(\alpha(a_i) = 1,\; \forall i)$ and rely on emergent coordination, not fusion.
• Centaurian Systems: A composite agent $C = (H, \text{AI}, \Phi)$, enveloping human ($H$) and AI ($\text{AI}$) subsystems, coupled through integration points $\Phi$ comprising shared cognitive representations, feedback loops, and decision procedures. Here autonomy is sacrificed for unified cognition $(\alpha(H \cup \text{AI}) < 1)$—no independent decision is made except via joint transitions.

This distinction determines the possible control flows, error propagation, and analytic tractability. MAS are suited for situations demanding distributed, decentralized decision-making and resilience, while Centaurian configurations serve domains in which the speed and reliability of joint human-AI cognition are paramount.

2. Communication-Space Architecture

Hybrid frameworks are instantiated via a three-layered communication architecture, applicable to both paradigms:

Surface Layer ($P_S$): Handles all interface-level events (UI actions, sensor streams, actuator commands). In MAS, agents merely relay data; in Centaurian systems, preliminary data fusion (e.g. intent extraction) may occur.

Observation Layer ($P_O$): Segregates input preprocessing, normalization, parsing, and routing. Responsible for transforming raw surface events into interoperable, semantically consistent tokens (e.g. "parsedCmd", "stateUpdate"). Ensures that mixed human-AI inputs are standardized prior to decision logic.

Computation Layer ($P_C$): Manages core reasoning, planning, and task assignments. In MAS, computation proceeds via coordinated agent subnetworks exchanging tokens (e.g., planning or approval tokens); in Centaurian systems, joint transitions require the co-presence of human and machine "sign-offs" for action completion.

Formally, the communication space is the union $P = P_S \cup P_O \cup P_C$, each disjoint and collectively exhaustive.

3. Formal Representations: Colored Petri Nets and Reconfigurable Networks

Centaurian Representation: Structured as colored Petri nets $(P, T, A, \Sigma, V, C, G, M_0)$, partitioned by communication layer. Transitions are guarded by Boolean conditions on the presence and type (color) of tokens, and only fire when composite preconditions (e.g., both a "planningTk" and an "approvalTk") are satisfied. The token colors encode data-structures, agent roles, and message semantics. An example transition in the computation layer is:

$$t_{\text{assign}}: \bullet t = \{p_{\text{plan}}, p_{\text{appr}}\},\, C(p_{\text{plan}}) = \{\text{planningTk}\},\, C(p_{\text{appr}}) = \{\text{approvalTk}\},\, G(t) = (\text{token}(p_{\text{plan}}) = \text{planningTk} \wedge \text{token}(p_{\text{appr}}) = \text{approvalTk})$$

MAS Representation: Modeled as a dynamic graph $G(t) = (V(t), E(t))$, with reconfiguration operators $\oplus,\ \ominus$ for agent addition/removal, and protocol sets $\Pi$ enforcing conditional message delivery. Agent lifecycle is algebraically captured as $G(t+1) = G(t) \oplus \text{addAgent}(a)$ or $G(t+1) = G(t) \ominus \text{removeAgent}(a)$.

Trade-offs: Colored Petri nets afford precise formal analysis of reachability, liveness, and composition, but struggle with highly dynamic team composition—a limitation addressed through high-level reconfigurable networks, which trade static structure for adaptability.

4. Domain Use Cases

4.1 Satellite-Swarm Robotics (Hybrid MAS-Centaurian):

• Surface: Sensor feeds and operator commands from drones via wireless links.
• Observation: LLM interpreter for mission directives; blockchain-backed message bus for routing.
• Computation: Satellite control unit executes an optimization algorithm only after receiving a human-LLM "approval token," temporarily forming a Centaurian micro-system within the MAS for critical decision junctures.

4.2 Large Action Models (LAMs, Centaurian):

• Surface: Human interface traces (keystrokes, GUI snapshots) become action tokens.
• Observation: Symbolic encoder produces intent tokens from action tokens.
• Computation: Neuro-symbolic rules predict next actions based on both intent and contextual tokens; ongoing human feedback updates context tokens in real time.

These scenarios demonstrate the flexible switching between protocol-enforced autonomy (MAS) and cognitive fusion (Centaurian), deploying either paradigm according to the requirements of real-time responsiveness, safety, and adaptive control.

5. Theoretical Advances and Open Challenges

Key Advances:

• Formal unification of MAS and Centaurian systems using layered communication spaces, permitting seamless composition of heterogeneous agent collectives.
• Mechanisms for preserving autonomy or enforcing fusion at critical "decision chokepoints," with modular architecture for clear analytic separation of input, transformation, and reasoning layers.
• Applicability to domains such as robotics, human-in-the-loop decision making, and cognitive architectures.

Limitations and Open Research Questions:

• Static Petri net structures fail to model dynamically shifting agent teams; hybrid frameworks require higher-order reconfiguration formalisms to fluidly register/deregister agents.
• Desire for purely set-theoretic interaction mappings, which abstract away explicit network topologies.
• Enhanced ethical, privacy, and transparency mechanisms in Centaurian systems, requiring audit tokens or guard transitions within formal models.
• Scalability bottlenecks: As the number of colors, places, and agents grows, computational tractability for reachability and verification may degrade, necessitating compositional and modular analysis techniques.

6. Implementation Considerations and Future Directions

Computational Requirements:

• MAS-mode systems require efficient graph-update operations and protocol enforcement, favoring distributed, message-passing infrastructures compatible with dynamic agent lifecycles.
• Centaurian-mode systems favor modular Petri net simulation engines capable of tracking guard conditions and transition combinatorics, with emphasis on data token typing and hybrid feedback integration.

Deployment Strategies:

• Hybrid frameworks must permit mode-switching, enabling systems to operate in MAS for routine coordination, but engage Centaurian fusion for critical, high-stake decision events.
• Integration with external data sources (sensor streams, human inputs), modular agent architecture, and real-time data normalization are essential for robust deployment.

Verification and Analysis:

• Use of compositional reachability analysis for colored Petri nets.
• Graph-theoretical monitoring of MAS reconfiguration dynamics.
• Incorporation of audit logs and formal certification for ethical and transparency compliance.

Significance:

Hybrid human-AI frameworks structured as communication-space Petri nets and high-level agentic network graphs enable precise engineering of next-generation collectives that balance structured protocol with emergent, fused cognition. This duality supports both the robustness of distributed agent ecosystems and the synergies of deeply integrated human-AI "organisms," with clear formal foundations for adaptive design, rigorous analysis, and verifiable operation in complex environments (Borghoff et al., 19 Feb 2025).