From b3528548d053239cb93d8f5dde079d3abdc2321e Mon Sep 17 00:00:00 2001
From: blackboxprogramming
 <118287761+blackboxprogramming@users.noreply.github.com>
Date: Fri, 8 Aug 2025 13:14:50 -0700
Subject: [PATCH] Update adaptive_learning.py

---
 hybrid/adaptive_learning.py | 45 ++++++++++++++++++++++++++++++++++++-
 1 file changed, 44 insertions(+), 1 deletion(-)

diff --git a/hybrid/adaptive_learning.py b/hybrid/adaptive_learning.py
index ad35e5a..4513e19 100644
--- a/hybrid/adaptive_learning.py
+++ b/hybrid/adaptive_learning.py
@@ -1 +1,44 @@
-print("Hello World")
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Callable, Any, Dict, List
+
+@dataclass
+class AdaptationRule:
+    """
+    Represents an adaptation rule with a trigger condition and action transformation.
+    """
+    trigger: Callable[[Dict[str, Any]], bool]
+    action: Callable[[Dict[str, Any]], Dict[str, Any]]
+
+class AdaptiveLearner:
+    """Applies adaptation rules to a context."""
+
+    def __init__(self) -> None:
+        self.rules: List[AdaptationRule] = []
+
+    def add_rule(self, rule: AdaptationRule) -> None:
+        """
+        Register a new adaptation rule.
+        """
+        self.rules.append(rule)
+
+    def adapt(self, context: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Apply the first rule whose trigger matches the context and return the modified context.
+        If no rule triggers, return the context unchanged.
+        """
+        for rule in self.rules:
+            if rule.trigger(context):
+                return rule.action(context)
+        return context
+
+if __name__ == "__main__":
+    learner = AdaptiveLearner()
+    learner.add_rule(
+        AdaptationRule(
+            trigger=lambda c: c.get("state") == "stuck",
+            action=lambda c: {**c, "assist": True},
+        )
+    )
+    print(learner.adapt({"state": "stuck"}))