PDF Computer Logic and Symbolic Reasoning ~ Wainaina MACHINE LEARNING nashon onyalo

Graphplan takes a least-commitment approach to planning, rather than sequentially choosing actions from an initial state, working forwards, or a goal state if working backwards. Satplan is an approach to planning where a planning problem is reduced to a Boolean satisfiability problem. Qualitative simulation, such as Benjamin Kuipers’s QSIM,[89] approximates human reasoning about naive physics, such as what happens when we heat a liquid in a pot on the stove. We expect it to heat and possibly boil over, even though we may not know its temperature, its boiling point, or other details, such as atmospheric pressure.

The purpose of this paper is to generate broad interest to develop it within an open source project centered on the Deep Symbolic Network (DSN) model towards the development of general AI. Symbolic AI is one of the earliest forms based on modeling the world around us through explicit symbolic representations. This chapter discussed how and why humans brought about the innovation behind Symbolic AI. The primary motivating principle behind Symbolic AI is enabling machine intelligence. Properly formalizing the concept of intelligence is critical since it sets the tone for what one can and should expect from a machine.

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For now, neuro-symbolic AI combines the best of both worlds in innovative ways by enabling systems to have both visual perception and logical reasoning. And, who knows, maybe this avenue of research might one day bring us closer to a form of intelligence that seems more like our own. A neuro-symbolic system, therefore, applies logic and language processing to answer the question in a similar way to how a human would reason. An example of such a computer program is the neuro-symbolic concept learner (NS-CL), created at the MIT-IBM lab by a team led by Josh Tenenbaum, a professor at MIT’s Center for Brains, Minds, and Machines. Neural networks are trained to identify objects in a scene and interpret the natural language of various questions and answers (i.e. “What is the color of the sphere?”).

The practice showed a lot of promise in the early decades of AI research. But in recent years, as neural networks, also known as connectionist AI, gained traction, symbolic AI has fallen by the wayside. The two biggest flaws of deep learning are its lack of model interpretability (i.e. why did my model make that prediction?) and the amount of data that deep neural networks require in order to learn. Despite these limitations, symbolic AI has been successful in a number of domains, such as expert systems, natural language processing, and computer vision. A second flaw in symbolic reasoning is that the computer itself doesn’t know what the symbols mean; i.e. they are not necessarily linked to any other representations of the world in a non-symbolic way.

How does symbolic AI differ from other AI approaches?

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It emphasizes logical reasoning, manipulating symbols, and making inferences based on predefined rules. Symbolic AI is typically rule-driven and uses symbolic representations for problem-solving.Neural AI, on the other hand, refers to artificial intelligence models based on neural networks, which are computational models inspired by the human brain. Neural AI focuses on learning patterns from data and making predictions or decisions based on the learned knowledge. It excels at tasks such as image and speech recognition, natural language processing, and sequential data analysis. Neural AI is more data-driven and relies on statistical learning rather than explicit rules.

The Rise and Fall of Symbolic AI

Following this, we can create the logical propositions for the individual movies and use our knowledge base to evaluate the said logical propositions as either TRUE or FALSE. Despite these challenges, symbolic AI continues to be an active area of research and development. It has evolved and integrated with other AI approaches, such as machine learning, to create hybrid systems that combine the strengths of both symbolic and statistical methods. Deep learning fails to extract compositional and causal structures from data, even though it excels in large-scale pattern recognition. While symbolic models aim for complicated connections, they are good at capturing compositional and causal structures. Another benefit of combining the techniques lies in making the AI model easier to understand.

The limits it has are similar to the limits that exist on any programming language. Any given language can do what any other language can do, but sometimes it is harder to do some tasks in a given language. Rather than detail the theory in a mathematical way, let’s look at a simple problem using First Order Logic (FOL). A Symbolic AI system is said to be monotonic – once a piece of logic or rule is fed to the AI, it cannot be unlearned. Newly introduced rules are added to the existing knowledge, making Symbolic AI significantly lack adaptability and scalability.

To properly understand this concept, we must first define what we mean by a symbol. The Oxford Dictionary defines a symbol as a “Letter or sign which is used to represent something else, which could be an operation or relation, a function, a number or a quantity.” The keywords here represent something else. We use symbols to standardize or, better yet, formalize an abstract form. At face value, symbolic representations provide no value, especially to a computer system. However, we understand these symbols and hold this information in our minds.

I tried ingesting the facts of the case into BERT and asking questions such as who is the appellant? Although BERT was sometimes able to locate the answers in the text and locate substrings of the text, this is far from actually understanding and retrieving information. In essence, I found that that was a very sophisticated information retrieval system but did not come close to the complexity needed to model the real world. One of the seminal attempts to apply statutory reasoning is the American-British logician Robert Kowalski and others’ modelling of the British Nationality Act as a logic program, which was published in 1986.

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• The symbolic side recognizes concepts such as “objects,” “object attributes,” and “spatial relationship,” and uses this capability to answer questions about novel scenes that the AI had never encountered.
• These are not merely buzz words — they’re techniques that have literally triggered a renaissance of artificial intelligence leading to phenomenal advances in self-driving cars, facial recognition, or real-time speech translations.
• Therefore, implicit knowledge tends to be more ambiguous to explain or formalize.
• Like in so many other respects, deep learning has had a major impact on neuro-symbolic AI in recent years.
• However, the methodology and the mindset of how we approach AI has gone through several phases throughout the years.