AI Agents with LangChain & Autonomous Workflows · Pelajaran

Arsitektur Kognitif untuk Agen

Pelajari secara mendalam arsitektur kognitif yang telah mapan, seperti SOAR dan ACT-R, yang memodelkan proses penalaran dan pembelajaran menyerupai manusia pada agen kecerdasan buatan.

Arsitektur Kognitif untuk Agen adalah pelajaran AI Agents with LangChain & Autonomous Workflows gratis di CoddyKit. Ini adalah pelajaran 4 dari 6. Kamu bisa membaca pelajaran lengkapnya di bawah secara gratis — lalu praktikkan langsung di browser dengan editor kode bawaan dan tutor AI 24/7. Ini adalah bagian dari jalur belajar AI Agents with LangChain & Autonomous Workflows, dan progresmu tersinkronisasi di web dan aplikasi CoddyKit. Kursus AI Agents with LangChain & Autonomous Workflows mencakup 6 pelajaran total.

Bagian dari pelajaran ini belum diterjemahkan dan ditampilkan dalam bahasa Inggris.

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.

Pertanyaan yang Sering Diajukan

Apakah pelajaran “Arsitektur Kognitif untuk Agen” gratis?

Ya — teks lengkap “Arsitektur Kognitif untuk Agen” gratis dibaca di sini di web. Untuk praktiknya secara interaktif (editor kode bawaan dan tutor AI 24/7) dan buka sisa kursus AI Agents with LangChain & Autonomous Workflows, upgrade ke CoddyKit PRO. Kursus AI Agents with LangChain & Autonomous Workflows mencakup 6 pelajaran total.

Apa yang akan aku pelajari di “Arsitektur Kognitif untuk Agen”?

Pelajari secara mendalam arsitektur kognitif yang telah mapan, seperti SOAR dan ACT-R, yang memodelkan proses penalaran dan pembelajaran menyerupai manusia pada agen kecerdasan buatan. Kamu berlatih AI Agents with LangChain & Autonomous Workflows dengan kode praktik yang langsung kamu jalankan di browser, dan tutor AI 24/7 menjawab pertanyaanmu saat kamu mengerjakan pelajaran ini.

Apakah aku perlu pengalaman untuk memulai AI Agents with LangChain & Autonomous Workflows?

Tidak diperlukan pengalaman sebelumnya. AI Agents with LangChain & Autonomous Workflows di CoddyKit dirancang untuk pemula hingga pelajar tingkat lanjut, jadi kamu bisa memulai di sini atau dari awal dan belajar sesuai kecepatan kamu sendiri. Ini adalah pelajaran 4 dari 6.

Berapa lama pelajaran “Arsitektur Kognitif untuk Agen” memakan waktu?

Sebagian besar pelajaran CoddyKit memakan waktu sekitar 5–10 menit. Setiap pelajaran ringkas dan interaktif, jadi kamu membuat kemajuan stabil dan melanjutkan dari tempat kamu tinggalkan di web dan aplikasi.

Bisakah aku menulis dan menjalankan kode dalam pelajaran AI Agents with LangChain & Autonomous Workflows ini?

Ya. Setiap pelajaran AI Agents with LangChain & Autonomous Workflows menyertakan editor kode bawaan, jadi kamu menulis dan menjalankan kode nyata langsung di browser dan mendapatkan umpan balik AI instan — tidak diperlukan penyiapan lokal.

Semua pelajaran dalam kursus ini

← Kembali ke AI Agents with LangChain & Autonomous Workflows