Ajanlar için Bilişsel Mimariler
Yapay zeka etmenlerindeki insan benzeri akıl yürütme ve öğrenme süreçlerini modelleyen yerleşik bilişsel mimarileri (ör. SOAR, ACT-R) ayrıntılı olarak inceleyin.
Ajanlar için Bilişsel Mimariler, CoddyKit'te ücretsiz bir AI Agents with LangChain & Autonomous Workflows dersidir. Bu, 6 dersinin 4. dersidir. Aşağıdan dersin tamamını ücretsiz okuyabilir, sonra tarayıcıda yerleşik kod editörü ve 7/24 yapay zeka koçu ile uygulamalı olarak pratik yapabilirsin. Bu, AI Agents with LangChain & Autonomous Workflows öğrenme yolunun bir parçasıdır ve ilerlemeniz web ve CoddyKit uygulaması arasında senkronize olur. AI Agents with LangChain & Autonomous Workflows kursu toplamda 6 dersten oluşur.
Bu dersin bazı bölümleri henüz çevrilmemiş olup İngilizce olarak gösterilmektedir.
Intro to Cognitive Agents
Welcome! Today, we'll explore Cognitive Architectures. These aren't just simple programs; they're frameworks designed to mimic human-like reasoning, learning, and decision-making in AI agents.
They bridge the gap between reactive agents and truly intelligent systems capable of complex problem-solving.
Why Cognitive Architectures?
Simple agents react to immediate percepts. But what if an agent needs to plan, learn from mistakes, or understand complex situations?
- Human-like Intelligence: Model how humans think.
- General Problem Solving: Tackle diverse tasks, not just one.
- Learning & Adaptation: Improve performance over time.
- Robustness: Handle unexpected situations.
Core Components of Cognition
Most cognitive architectures share common building blocks, inspired by human psychology:
- Perceptual System: How the agent "sees" the world.
- Memory Systems: Short-term (working) and long-term knowledge.
- Decision-Making: How the agent chooses its next action.
- Motor System: How the agent acts on the world.
SOAR: State, Operator, Result
SOAR (State, Operator, And Result) is a classic cognitive architecture. It views all intelligence as a continuous process of problem-solving, represented as searching through a state space.
SOAR operates in decision cycles, constantly choosing operators to apply to the current state to reach a desired result.
SOAR's Working Memory
SOAR's working memory holds the agent's current understanding of the world, its goals, and the current problem state. It's temporary and constantly updated.
Think of it as the agent's "consciousness" at any given moment. Here's a simplified representation:
public class SoarWorkingMemory {
String goal;
String currentState;
boolean obstacleDetected;
public SoarWorkingMemory(String goal, String state) {
this.goal = goal;
this.currentState = state;
this.obstacleDetected = false;
}
public void updateState(String newState) {
this.currentState = newState;
}
public String toString() {
return "Goal: " + goal + ", State: " + currentState +
", Obstacle: " + obstacleDetected;
}
public static void main(String[] args) {
SoarWorkingMemory wm = new SoarWorkingMemory("ReachExit", "StartRoom");
System.out.println(wm);
wm.updateState("Corridor");
System.out.println(wm);
}
}SOAR's Production Rules
SOAR uses production rules (if-then rules) in its long-term memory to propose and select operators. When a rule's if condition matches the working memory, its then part proposes an action or modifies the state.
This example shows a simple rule for moving an agent:
public class SoarProductionRule {
public static void main(String[] args) {
String currentState = "near_door";
String goal = "exit_room";
System.out.println("Current State: " + currentState);
System.out.println("Goal: " + goal);
// A simple SOAR-like production rule
if (currentState.equals("near_door") && goal.equals("exit_room")) {
System.out.println("Rule Fired: Propose 'open_door_operator'");
System.out.println("Action: Agent opens the door.");
currentState = "door_open"; // State update
} else {
System.out.println("No matching rule fired.");
}
System.out.println("New State: " + currentState);
}
}SOAR's Learning: Chunking
A unique feature of SOAR is chunking. When the agent encounters an impasse (a situation where it can't decide what to do), it enters a sub-state to resolve it.
Once the impasse is resolved, SOAR "chunks" the experience, creating a new production rule that directly solves that type of impasse in the future. This is how SOAR learns!
ACT-R: Adaptive Control of Thought
ACT-R (Adaptive Control of Thought—Rational) is another prominent cognitive architecture. It's designed to model human cognition at a finer grain, focusing on psychological data and predicting human behavior.
ACT-R emphasizes a modular structure, with distinct memory systems and processes working together.
ACT-R's Memory Modules
ACT-R has several key modules, including:
- Declarative Memory: Stores factual knowledge (e.g., "Paris is the capital of France") as discrete units called chunks.
- Procedural Memory: Stores "how-to" knowledge (e.g., "how to tie a shoe") as production rules.
- Goal Module: Manages the agent's current goals.
- Imaginal Module: Holds temporary problem representations.
Activation & Utility in ACT-R
Unlike SOAR's pure rule-matching, ACT-R's modules interact based on activation and utility:
- Activation: Chunks in declarative memory have an activation level, influencing how quickly they can be retrieved. More relevant or recent chunks have higher activation.
- Utility: Production rules in procedural memory have a utility value, reflecting their past success. Rules with higher utility are more likely to be chosen.
Cognitive Arch. Check
Let's check your understanding of these cognitive architectures.
Recap: Cognitive Agents
We've explored Cognitive Architectures, frameworks that aim for human-like intelligence. We looked at:
- SOAR: Problem-solving as search, using production rules and learning via chunking.
- ACT-R: A modular system with declarative and procedural memory, guided by activation and utility.
These architectures provide powerful models for building agents that can reason, learn, and adapt in complex ways.
Sıkça Sorulan Sorular
“Ajanlar için Bilişsel Mimariler” dersi ücretsiz mi?
Evet — “Ajanlar için Bilişsel Mimariler” dersin tüm metni burada web'de ücretsiz olarak okunabilir. Etkileşimli olarak pratik yapmak (yerleşik kod editörü ve 7/24 yapay zeka koçu) ve AI Agents with LangChain & Autonomous Workflows kursunun geri kalanını açmak için CoddyKit PRO'ya yükselt. AI Agents with LangChain & Autonomous Workflows kursu toplamda 6 dersten oluşur.
“Ajanlar için Bilişsel Mimariler” dersinde ne öğreneceğim?
Yapay zeka etmenlerindeki insan benzeri akıl yürütme ve öğrenme süreçlerini modelleyen yerleşik bilişsel mimarileri (ör. SOAR, ACT-R) ayrıntılı olarak inceleyin. AI Agents with LangChain & Autonomous Workflows ile uygulamalı kodu tarayıcıda doğrudan çalıştırarak pratik yaparsın ve 7/24 yapay zeka koçu dersi çalışırken sorularını yanıtlar.
AI Agents with LangChain & Autonomous Workflows öğrenmeye başlamak için deneyim gerekli mi?
Önceden deneyim gerekmez. CoddyKit'te AI Agents with LangChain & Autonomous Workflows, başlangıçtan ileri seviyeye kadar yapılandırıldığı için buradan başlayabilir veya başından başlayıp kendi hızında ilerleme yapabilirsin. Bu, 6 dersinin 4. dersidir.
“Ajanlar için Bilişsel Mimariler” dersi ne kadar sürer?
Çoğu CoddyKit dersi yaklaşık 5–10 dakika sürer. Her biri kısa ve etkileşimli olduğu için sabit ilerleme yaparsın ve web ile uygulama arasında tam olarak bıraktığın yerden devam edebilirsin.
Bu AI Agents with LangChain & Autonomous Workflows dersinde kod yazıp çalıştırabilir miyim?
Evet. Her AI Agents with LangChain & Autonomous Workflows dersi yerleşik bir kod editörü içerir, bu sayede tarayıcıda gerçek kod yazıp çalıştırabilir ve anlık yapay zeka geri bildirimi alırsın — yerel kurulum gerekli değildir.
Bu kursun tüm dersleri
- ReAct ve Planla-Uygula Aracıları
- Hiyerarşik Ajan Tasarımları
- Kendi Kendini Düzelten ve Yansıtan Aracılar
- Ajanlar için Bilişsel Mimariler
- Çok Aracılı İş Birliği Örüntüleri
- Hibrit Ajan Sistemleri