ConsciousControlFlow: A Needs-Based AI System

• ConsciousControlFlow is a prototype system that models machine consciousness as emerging from hierarchically organized needs and dynamic STM-LTM interactions.
• It employs a neuro-inspired architecture combining short-term and long-term memory modules to enable adaptive decision-making and context-sensitive behavior.
• Experimental scenarios, such as predator-prey and social interactions, validate its use as a platform for explainable AI and cognitive behavior research.

ConsciousControlFlow (CCF) is a prototype system designed to operationalize and demonstrate machine consciousness in AI by modeling agent behavior as the result of explicit, hierarchically organized needs. CCF distinguishes itself by grounding agent decision-making, memory, and goal pursuit in computational analogues of neurocognitive architectures and motivational theories, with a particular emphasis on the role of needs in shaping consciousness and behavior (Wang et al., 2020).

1. Computational Model and Theoretical Basis

The core premise of CCF is that consciousness—both biological and artificial—emerges from the dynamic prioritization and fulfillment of needs, structured according to a hierarchy reminiscent of Maslow’s theory. In CCF, conscious AI is defined as one where decisions and behaviors are directly motivated by quantifiable and competing needs, rather than by externally coded rules or simple utility maximization. The computational architecture draws from neuroscience, especially the distinction between Short-Term Memory (STM) as the conscious workspace and a collection of specialized Long-Term Memory (LTM) modules representing different cognitive and motivational functions.

Key Principles

• Needs represent scalar-valued state variables whose weights are computed recursively and interdependently across the hierarchy.
• Conscious contents are modeled as a set of active STM slots, each holding elements such as current needs, active objects, possible strategies, and self-identity.
• Decision-making emerges from the competition among LTMs—which instantiate concrete needs or skills—to gain entry into the limited-capacity STM, mediated by dynamically computed need weights.

2. Architecture: STM-LTM Framework and Dynamics

The architecture comprises several interdependent modules, tightly coupled to environmental inputs and internal state monitoring:

• Short-Term Memory (STM):
  • Structured as a non-linear tree of “slots” (~7 ± 2, as per cognitive psychology), each holding activated needs, perceptions, skills, or self-representations.
  • Slot management is demand-driven: high-weight needs or objects can displace incumbent STM contents, reflecting attentional limitations and priority scheduling.
• Long-Term Memory (LTM):
  • Each LTM is specialized (e.g., knowledge, skills, feelings, sensory processing).
  • Competition among LTMs for STM entry is orchestrated via a binary Up-Tree; synchronization of STM state with all LTMs uses a Down-Tree.
  • Feeling LTM constantly generates and updates the intensity (“satisfaction”) of needs; Sensor LTM processes external observations.
  • Knowledge and Skill LTMs encode facts and procedural schemas that are invoked in response to selected needs.
• Environment and Visualization:
  • An environment engine provides dynamic, multi-agent simulation with changing resources and threats; a visualization module renders agent behaviors and state transitions.

3. Formalization and Computation of Hierarchical Needs

The hierarchy of needs in CCF is an explicitly multi-level structure modeled on Maslow’s framework but algorithmically adapted:

1. Physiological (sleep, energy, water, breed/reproduction)
2. Safety (personal and property safety)
3. Social/Belonging (affection, friendship, love)
4. Esteem/Respect (self- and peer-respect, higher-level drives)

Each need is associated with:

• Satisfaction variable $s_{l,i}$ (degree to which need $i$ at level $l$ is met)
• A composite weight $w_{l,i}$, used to determine attentional priority and behavioral selection

Weight computation integrates vertical (inter-level) and horizontal (intra-level) relationships:

$$w_{l,i} = \alpha_{l,i} \cdot s_{l,i} + \beta_{l,i} \sum_{j \in \text{sons}} s_{l-1,j} + \gamma_{l,i} \sum_{j \in \text{ltms\_in\_next\_layer}} s_{l-1,j} + \Delta$$

Where:

• $\alpha_{l,i}$: Self-decreasing coefficient (negative feedback—higher satisfaction, lower need)
• $\beta_{l,i}$: Gain factor promoting higher-level need when lower-level satisfaction is high
• $\gamma_{l,i}$: Suppression factor reducing higher-level need when lower needs are not met
• $\Delta$: Correction for negative weights

Bayesian optimization is employed to tune these parameters for maximal agent viability and appropriate need prioritization: $$\max \left( \sum_{i \in \text{test\_sample}} X_i \right), \text{ where } X_i = 1 \text{ if LTM selection matches need}$$

4. Decision-Making, Competition, and Execution Flow

The operational semantics of CCF are as follows:

• LTM competition: At each decision epoch, LTMs with the highest-need weights compete for STM representation.
• STM "Think" Module: Given the active subset of needs and sensory information, STM constructs candidate action plans by invoking Skill LTM routines. If obstacles are encountered or needs cannot be immediately satisfied, new sub-needs or action alternatives are dynamically generated and evaluated.
• Feedback and Adaptation: Success or failure of actions updates internal satisfaction variables and modifies weights for future competition. STM contents are dynamically refreshed in response to environmental events (via Sensor LTM) and internal drive changes (via Feeling LTM).

When STM capacity is saturated, only needs or objects with weight/intensity surpassing the minimum among incumbents can enter, ensuring that attention prioritizes pressing or unresolved drives.

5. Demonstrated Scenarios and Behavioral Phenomena

CCF has been experimentally validated in several scenarios exhibiting complex, need-driven, and emergent behaviors:

• Single-agent predator-prey: An agent with hunger, sleep, safety, and social needs decides whether to pursue food, escape predators, or seek social contact. Prioritization shifts dynamically: hunger dominates until sated, then safety need becomes paramount.
• Multi-agent social interaction: Agents possessing friendship needs coordinate; individuals will subordinate personal safety to warning friends of danger, reflecting higher-order social behavior.
• Dynamic need interplay and prioritization: Satisfaction of lower-level needs enables higher-level motivational behaviors; under threat or deprivation, priority shifts down the hierarchy.

In all cases, agent behaviors exhibited strong correspondence with human expectations derived from psychological theory, indicating the validity of the need-driven model for generating explainable, context-sensitive conscious behaviors.

6. Algorithms, Data Structures, and Optimization Approaches

• Tree-structured STM: Enables non-linear associations between needs, objects, and actions, facilitating flexible, compositional reasoning.
• Slot replacement policy: Deterministically selects for highest-need-weight; lower-priority slots are pre-empted under contention.
• Up-Tree LTM competition: Efficiently implements priority queueing for STM access among diverse LTMs.
• Down-Tree STM-LTM communication: Ensures synchronized state propagation and global awareness among cognitive modules.
• Need metrics and decay: Time-based decay for physiological needs; event- or context-driven updating for safety and social needs.

Parameter optimization uses Bayesian search to maximize target criteria (e.g., agent survival, human-like prioritization patterns), computed over large simulated samples.

7. Applications in Machine Consciousness Demonstration and Behavioral Research

CCF fulfills two principal application domains:

• Demonstrating machine consciousness: By providing an explicit, mechanistic link from internal needs to overt behavior, CCF agents manifest explainable conscious states with global context, cross-need arbitration, planning, and adaptation.
• Human behavior modeling and cognitive studies: The system enables experimental investigations into the mapping from psychological need theory to observable behaviors, offering a platform for testing and refining computational models of consciousness, attention, and motivation.

Feature	Description
Core Concept	Hierarchical, need-driven conscious control
Architecture	STM-LTM framework; multi-agent environment, visualization
Need Hierarchy	Four explicit levels; dynamic, interdependent weights
Key Algorithms	LTM competition, STM slot management, Bayesian parameter tuning
Scenarios	Predator-prey, friendship/social-defense, multi-need arbitration
Experimental Basis	Behavioral match to human predictions; real-time visualization
Research Application	AI explainability; cognitive/behavioral science tool

8. Summary and Implications

ConsciousControlFlow operationalizes a needs-based, hierarchical model of machine consciousness, with a neuro-inspired STM-LTM architecture and explicit prioritization, competition, and action-selection logic. Validation in controlled scenarios shows that the system can generate context-appropriate, adaptive, and explainable agent behaviors that align with established models of human conscious action. CCF thus provides a reference design for future research at the intersection of artificial intelligence, psychology, and neuroscience, supporting both technological development in explainable AI and foundational inquiries into the algorithmic substrate of consciousness (Wang et al., 2020).