8
votes
What are some examples of Classical AI applications?
The term classical AI refers to the concept of intelligence that was broadly accepted after the Dartmouth Conference and basically refers to a kind of intelligence that is strongly symbolic and ...
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5
votes
Is the expert system still in use today?
Are there companies that still use expert systems?
There are still some expert system inference engines available in open source form, in particular CLIPS rules
A specialization of your question ...
5
votes
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Is the expert system still in use today?
I would say Expert Systems is still being taught. For instance, if you look at some of the open courses like MIT's, there are still lectures on it.
Also, looking at the CLIPS documentation, you will ...
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5
votes
What kinds of problems can AI solve without using a deep neural network?
I was hoping to see more answers here, but I'll get us started with some examples:
Combinatorial Search Problems: If your problem can be phrased as movement through a combinatorial graph, you don't ...
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4
votes
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What kind of body (if any) does intelligence require?
This is something of an orthogonal answer, but I think Brooks didn't go about his idea the right way. That is, subsumption architecture is one in which the 'autopilot' is replaced by a more ...
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4
votes
Is anybody still researching GOFAI?
Sure! There's the whole Semantic Web scene! OWL is derived from DLs and Frames, arguably has a lot in common with semantic networks too. Expert-driven decision support systems are still being ...
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4
votes
Is anybody still researching GOFAI?
Oh yeah, definitely. Just to pick one example, you have Douglas Hofstader's group at Indiana. I think most of what they do would fall under the rubric of GOFAI (or at least closer to that than the ...
- 3,757
4
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What are the differences in scope between statistical AI and classical AI?
Statistical AI, arising from machine learning, tends to be more concerned with inductive thought: given a set of patterns, induce the trend.
Classical AI is the branch of artificial intelligence ...
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4
votes
What kinds of problems can AI solve without using a deep neural network?
A nice example Markov Decision Processes, which can be solved by classic reinforcement learning techniques like Q learning.
A Markov Decision Process consists of
A set of discrete states (or ...
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4
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Is there any AI system for finding the best way to schedule university classes?
Welcome to AI.SE @Israr Ali.
The problem of scheduling a timetable is an example of a constraint satisfaction problem, a topic long studied in AI.
There are many possible techniques to apply to this ...
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3
votes
Why is symbolic AI not so popular as ANN but used by IBM's Deep Blue?
I'm not sure any intelligent mechanism can be entirely free of symbolic logic.
Even where a decision is statistically based, a machine that takes actions must include some form of:
IF {some condition}...
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3
votes
Why is symbolic AI not so popular as ANN but used by IBM's Deep Blue?
You might also ask if there's any particular reason why we would use a neural net. If we're to train a neural net to play chess, we need to be able to:
1. Feed it positions as input vectors (easy ...
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3
votes
How to calculate the optimal placements for settlements in Catan without an ML algorithm?
Catan is actually a much more complicated game than the simple rules would suggest, and an exact solution is probably beyond the scope of current AI techniques.
Monte Carlo Tree Search or ...
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3
votes
Accepted
Shouldn't Gödel's incompleteness theorems disprove the physical symbol system hypothesis?
The PSSH is often attacked via either Godel's theorems or Turing's incomputability theorem.
However, both attacks have an implicit assumption: that to be intelligent is to be able to decide ...
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3
votes
Can (trained) neural networks be combined with symbolic AI to perform operations like AND?
I think you would be interested in Neural-Symbolic Learning and Reasoning, a recent survey on the intersection between connectionist models (e.g. neural nets) and symbolic reasoning. It's a long paper,...
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3
votes
What kinds of problems can AI solve without using a deep neural network?
Image Segmentation with Unsupervised Learning
Deep Learning is now widely used for image classification and segmentation. However, for segmentation, some algorithms are still really effective. For ...
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3
votes
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Which rules should I define for the predicate "not_to_far" of the exercise 1.1 of the book "Simply Logical: Intelligent Reasoning by Example"?
Your intuition is good. Because "nearby" is only defined with "connected", there could only be 1 station between them. However, it says that the stations are "not_too_far" if at most one station is ...
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3
votes
Is logic AI a complement to learning AI?
What you refer to as logic AI is a subset of what is called symbolic AI, as you manipulate symbols, according to certain rules (which could be rules of logic). These rules are either authored by a ...
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2
votes
How can thought vectors be used outside of an Artificial Neural Network (ANN) context?
I'll take a shot at answering this, though I'm no expert in Neural Nets or Deep Learning.
Given that practical thought vectors (TVs) don't yet exist, and may be impractical or impossible, I think ...
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votes
Can rule induction be considered a way to "hybridize" probabilistic / statistical approaches and symbolic approaches?
Sure! This is a somewhat hot area right now.
There are lots of ways to do it.
Probably the main line of research is with Bayesian Networks (1980's) and Casual Networks (1990's). These are basically ...
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2
votes
Historical weakness of GOFAI in relation to partisan combinatorial games?
Nice question!
I think there are a couple of issues at work here.
Is the historical weakness of GOFAI in relation to non-trivial
combinatorial games partly a function of the structure of the ...
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2
votes
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Is it possible to do K-nearest-neighbours before training DNN
There are two factors that will change the ability of a deep neural network to fit a given dataset: either you need more data, or a deeper and wider network. Since the pattern is only 2-d, it can ...
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2
votes
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Is explainable AI more feasible through symbolic AI or soft computing?
XAI is relevant to "black box" AI (machine learning methods where the decision making rationale is not apparent, only the structure of the system that led to that decision.)
Symbolic AI, GOFAI, and ...
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2
votes
Shouldn't Gödel's incompleteness theorems disprove the physical symbol system hypothesis?
Although there seems to be an apt analogy between Gödel's theorems and the PSHH, there is nothing formal linking the two together.
More concretely, Gödel's theorems are about systems that decide ...
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2
votes
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Why is symbolic AI not so popular as ANN but used by IBM's Deep Blue?
ANNs as used today need 1. a lot of data 2. a lot of computational power. Before we had any of the above two, we didn't really know how to properly build ANNs since we didn't quite have the means to ...
- 2,071
2
votes
How to calculate the optimal placements for settlements in Catan without an ML algorithm?
Historically, the non-ML approach would be an expert system. This is typically a rules-based decision system, falling under the umbrella of symbolic AI.
These systems can have strong utility in ...
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2
votes
Is there any other (possibly less popular) approach to create AI apart from statistical methods?
Yes, there is symbolic AI. This was the 'original' approach to AI, at a time when there was very little data and/or processing power available. The focus was on logic and calculus, not on machine ...
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1
vote
Is there any other (possibly less popular) approach to create AI apart from statistical methods?
What you're looking for are Expert systems and Knowledge Based Systems. Really similar to each other, they encompass all systems built upon experts knowledge, from which analytic rules are derived in ...
- 5,218
1
vote
Accepted
Do these FOL formula both represent "You can fool some of the people all of the time"?
(1) can be paraphrased as "There exists an x, and for any t if x is a person and t is a time, then x can be fooled at time t" (I would use fool-able instead of can-fool, as it is closer to ...
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1
vote
What are recent AI software systems and research papers close to J. Pitrat's ideas?
Today one of the challenges is learning representations/concepts that are causally invariant. Once we have good representations then we can work on the reasoning aspect. There are 2 camps of people ...
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