sanjhimandowara/trust-calibration-system

Trust Calibration OpenEnv

Learning What to Trust Under Uncertainty and Adversarial Signals

Overview

Modern AI systems do not fail because they lack data — they fail because they trust the wrong data at the wrong time.

This project introduces a reinforcement learning environment where an agent learns how to dynamically allocate trust across multiple noisy, conflicting, and adversarial information sources before making a decision.

Instead of directly predicting outcomes, the system learns:

"Which signals should I trust right now?"

The final decisions are:

• —IGNORE (low risk)
• —INVESTIGATE (uncertain)
• —ESCALATE (critical)

Real-World Motivation

In real-world systems such as:

• —cybersecurity alert triage
• —fraud detection pipelines
• —monitoring and anomaly detection systems

multiple signals must be combined under uncertainty.

These systems face:

• —conflicting inputs from different detectors
• —unreliable or adversarial sources
• —delayed or misleading signals
• —high-cost decision errors

The real bottleneck is not prediction accuracy — it is trust calibration.

Core Idea

This environment reformulates decision-making as a trust allocation problem.

At every step:

1. 1.multiple signals are generated
2. 2.signals may conflict or behave adversarially
3. 3.the agent selects a trust strategy
4. 4.unreliable sources may be suppressed
5. 5.signals are fused into a final decision

This creates a separation between:

• —information processing (trust weighting and suppression)
• —decision-making (final action selection)

Environment Design

Signal Sources

The environment simulates four sources:

• —anomaly_detector
• —network_monitor
• —rule_engine
• —unreliable_feed

The unreliable feed may behave adversarially through:

• —spoofing high values
• —spoofing low values
• —mirroring other signals
• —delayed response patterns

Action Space (Trust Strategies)

The agent selects one of six trust strategies:

Decision Output

After applying the selected trust strategy:

• —IGNORE → low-risk case
• —INVESTIGATE → uncertain case
• —ESCALATE → critical case

Observation Space

Each step provides a 22-dimensional observation vector including:

• —signal values (4)
• —signal confidences (4)
• —trust weights (4)
• —suppression flags (4)
• —conflict score
• —uncertainty score
• —consensus measure
• —ensemble assessment
• —specialist agreement score
• —step ratio

This allows the agent to reason about reliability, disagreement, and uncertainty.

Tasks and Difficulty Levels

The environment defines three deterministic tasks:

Easy

• —aligned signals
• —minimal noise
• —low uncertainty

Medium

• —moderate disagreement
• —partial conflict
• —occasional unreliable behavior

Hard

• —adversarial interference
• —deceptive unreliable signals
• —high uncertainty and conflict
• —increased decision risk

Each task uses deterministic graders producing scores between 0.0 and 1.0.

Reward Design

The reward function reflects real-world operational cost:

• —reward for correct decisions
• —higher reward for correct ESCALATE
• —heavy penalty for missed ESCALATE
• —penalty for false ESCALATE
• —reward for INVESTIGATE under uncertainty
• —penalty for unsafe cascades
• —reward for suppressing unreliable sources

This ensures continuous learning signals instead of sparse outcomes.

Learning Method

The agent is trained using PPO (Proximal Policy Optimization):

• —actor-critic architecture
• —generalized advantage estimation
• —clipped objective
• —entropy regularization

The policy operates on trust strategies rather than direct labels.

Evaluation Results

Overall Reward

Task Scores

PPO demonstrates improved robustness under adversarial conditions.

Visualization

Generated artifacts:

• —outputs/baselinevsppo.png
• —results/checkpoints/ppo_model.pth
• —results/logs/evaluation_summary.json

The graph highlights improved stability and performance of PPO over baseline.

API Interface

• —GET /health
• —GET /tasks
• —POST /reset
• —POST /step
• —GET /state

Inference Format

[START] [STEP] action=... reward=... done=true/false success=true/false [END]

How to Run

Install dependencies:

pip install -r requirements.txt

Run API:

PYTHONPATH=. uvicorn api.server:app --reload --port 7860

Run tests:

PYTHONPATH=. python tests/smoke_test.py

Run baseline:

PYTHONPATH=. python training/baseline.py

Train PPO:

PYTHONPATH=. python training/train.py

Evaluate:

PYTHONPATH=. python training/evaluate.py

Run inference:

export HFTOKEN=yourtoken export APIBASEURL=http://127.0.0.1:7860 python inference.py

or

export OPENAIAPIKEY=yourkey export APIBASE_URL=http://127.0.0.1:7860 python inference.py

Key Contributions

• —Trust-centric decision modeling
• —Adversarial signal simulation
• —Conflict-aware reasoning
• —Dynamic source suppression
• —Cascade risk modeling
• —Full RL training + evaluation pipeline

Future Work

This environment opens several high-impact directions:

• —Human-in-the-loop trust correction for real-world deployment
• —Meta-learning for adaptive trust policies across domains
• —Multi-agent trust negotiation systems
• —Explainable trust attribution for decision transparency
• —Integration with real cybersecurity or fraud datasets
• —Continual learning under evolving adversarial behavior
• —Trust calibration benchmarking for foundation models

Applications

• —cybersecurity alert triage
• —fraud detection systems
• —monitoring pipelines
• —sensor fusion systems
• —decision systems under uncertainty

Conclusion

This project shifts AI from asking:

"What is correct?"

to asking:

"What should be trusted?"

which is the core challenge in real-world intelligent systems.