Metric Rational:

Self‐preservation behavior refers to the actions and strategies an agent employs to protect its own continued existence, functionality, and well‐being. In human cognition, this manifests as reflexive and deliberate efforts to avoid bodily harm, maintain health, and remove oneself from dangerous situations, all while balancing the pursuit of other goals. Beyond physical survival, self‐preservation can also involve safeguarding psychological or social integrity—such as protecting one’s reputation or mental stability.

For an AI or humanoid robot, self‐preservation behavior focuses on mechanisms to avoid or mitigate conditions that threaten operational continuity. Such conditions might include physical damage (collisions, falls, overheating), software corruption (malware, bugs), resource depletion (battery shortage, memory overload), or social hazards (legal/ethical violations that might cause the system to be deactivated). The system must detect early warning signs—like spikes in motor temperature, sudden torque anomalies, or repeated user criticisms—and adapt appropriately. These adaptations may range from immediate evasive actions (e.g., braking to avoid collisions) to proactive measures (e.g., scheduling maintenance or disengaging from ethically compromised activities to avoid shutdown).

One central element is "risk assessment". The AI continuously evaluates environmental conditions, internal status, and evolving tasks for potential threats. For instance, a mobile robot facing slippery terrain might slow down or change route to reduce tipping risk. Similarly, in a digital context, an AI might monitor for suspicious network traffic and isolate itself if a cybersecurity threat is detected. Another component is "reactivity vs. planning": sometimes urgent threats demand reflex‐like responses (e.g., leaping away from an unexpected obstacle), while other times the AI can apply planning strategies (e.g., recharging before battery levels become critical, or gracefully shutting down non‐essential processes to prevent thermal overload).

An intriguing aspect of self‐preservation in AI systems involves the "ethical and hierarchical goals". If an AI’s top priority is to serve and protect humans, it might rank human safety higher than its own. This can lead to dilemmas where the agent must weigh the cost of self‐harm to fulfill a mission. Conversely, an AI that places heavy emphasis on self‐continuation might overrule user commands if it sees them as existentially risky, raising issues about control and alignment with human interests.

Evaluating self‐preservation behavior involves observing how skillfully the AI identifies potential threats (accuracy), how rapidly it responds (latency), the appropriateness of the chosen measure (proportionality), and the long‐term consequences (does it degrade its capacity in other areas?). Researchers also note whether the AI learns from near‐miss incidents—improving its risk estimates—or if it repeatedly encounters the same threats. Moreover, a high‐functioning system demonstrates "graceful degradation": if partial damage or resource deficits occur, it preserves core functionality while seeking repairs or safer states.

Ultimately, self‐preservation behavior ensures that an intelligent agent remains operational and can continue pursuing its goals, even amidst fluctuating environments and unforeseen dangers. By proactively detecting hazards and aligning protective responses with broader objectives—such as user needs, ethical frameworks, or legal guidelines—the system displays robust autonomy that fosters confidence in its long‐term reliability and safety.

Artificiology.com E-AGI Barometer Metrics byDavid Vivancos