Symbolic Artificial Intelligence
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Symbolic Artificial Intelligence refers to AI approaches that are based on explicit symbol manipulation. This includes things like term rewriting, graph algorithms, and natural language question answering. It is often more narrowly understood as a reference to methods based on formal logic, as utilized in the subfield of AI called Knowledge Representation and Reasoning. The collection of all methods in artificial intelligence research that are based on high-level symbolic (human-readable) representations of problems, logic and search. Symbolic AI used tools such as logic programming, production rules, semantic nets and frames, and it developed applications such as knowledge-based systems (in particular, expert systems), symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated planning and scheduling systems. The Symbolic AI paradigm led to seminal ideas in search, symbolic programming languages, agents, multi-agent systems, the semantic web, and the strengths and limitations of formal knowledge and reasoning systems. Symbolic Artificial Intelligence | Wikipedia
Intelligent behavior can be achieved by using explicit symbols and rules to process and manipulate information.
Focuses on the representation and manipulation of knowledge and symbols to simulate human-like reasoning and problem-solving.
- Symbolic representation: In Symbolic AI, knowledge is represented using symbols, which can be thought of as abstract representations of objects, concepts, relationships, or facts. These symbols can be organized into structures such as logic-based languages, ontologies, or semantic networks to capture complex relationships and hierarchies.
- Symbolic reasoning: Symbolic AI systems perform reasoning by manipulating symbols and applying logical rules to draw inferences, make decisions, and solve problems. This form of reasoning is often based on formal logic, where propositions and rules are expressed using logical formulas and can be combined and evaluated to derive new conclusions.
- Knowledge representation and inference: Symbolic AI emphasizes the development of formal languages and frameworks for knowledge representation and inference. These languages allow the encoding of domain-specific knowledge and facilitate the application of logical reasoning techniques to derive new knowledge or answers to queries.
- Expert systems: Expert systems are a prominent application of Symbolic AI. These systems emulate the expertise of human specialists in specific domains by representing their knowledge as rules and using inference mechanisms to provide advice, make diagnoses, or solve complex problems.
- Limitations and challenges: Symbolic AI has faced challenges in handling uncertainty, ambiguity, and the complexity of real-world problems. The reliance on explicit symbols and rules can make it difficult to represent and reason about uncertain or incomplete information. Additionally, the scalability and computational efficiency of symbolic systems can be limitations when dealing with large-scale, data-intensive tasks.
GOFAI stands for "Good Old-Fashioned Artificial Intelligence". It's a classical symbolic approach to AI that views the brain as a machine for processing symbols. GOFAI is also known as "symbolicism" because it attempts to describe intelligence in symbolic terms. GOFAI was born out of the Dartmouth conference in 1956. It was characterized by a nearly exclusive focus on symbolic reasoning and logic. The fundamental underlying issue with GOFAI is the relative cost of optimization power. GOFAI is different from other approaches to AI, such as neural networks, situated robotics, narrow symbolic AI, or neuro-symbolic AI.
Symbolic Artificial Intelligence provides a powerful framework for knowledge representation, logical reasoning, and problem-solving. While it has its limitations, Symbolic AI has contributed significantly to fields such as expert systems, natural language processing, and automated reasoning. By combining symbolic approaches with other AI techniques, such as machine learning, researchers aim to create more comprehensive and robust AI systems that can handle the complexity and uncertainty of real-world problems.