Problem solving

Problem solving
Problem solving forms part of thinking. It occurs if an organism or an artificial intelligence system does not know how to proceed from a given state to a desired goal state.

Cellection - A Problem Solving Framework for Computational Functions
By P. GopalaKrishna ...

3 The Blackboard Architecture for Problem Solving 187
6.4 Epilogue and References 219
6.5 Exercises 220
PART III CAPTURING INTELLIGENCE: THE AI CHALLENGE 223
7 KNOWLEDGE REPRESENTATION 227
7.0 Issues in Knowledge Representation 227
7.

Problem solving. In general, problem solving is not a creative activity (although Stravinsky thought it was -- we will return to his view and his rather different definition of "problem solving").

Problem Solving & Optimisation
To solve a problem, a solution needs to be encoded as a genotype - or sequence of genes. For humans, this can be understood as a chromosome.

General Problem Solving Approaches in AI
To understand what exactly artificial intelligence is, we illustrate some common problems. Problems dealt with in artificial intelligence generally use a common term called 'state'.

Problem solving and planning
Newell, Shaw and Simon's General Problem Solver (GPS, 1963);
Nilsson & Fike's Stanford Research Institute Problem Solver (STRIPS, 1971) --- controlled a mobile robot called Shakey
Perception ...

A problem solving procedure that starts with a set of rules and a database of facts and works to a conclusion based on facts that match all the premises set forth in the rules.
If-Then Rule ...

^ Problem solving, puzzle solving, game playing and deduction:
Russell & Norvig 2003, chpt. 3-9,
Poole, Mackworth & Goebel 1998, chpt. 2,3,7,9,
Luger & Stubblefield 2004, chpt. 3,4,6,8,
Nilsson 1998, chpt. 7-12 ...

Real-Time Problem Solving
Rule-Based Reasoning
If you see a flying object in the sky, and it has wings, and it is not an airplane, and the time-of-day is daytime, then it is a bird (with certainty > 99%).

Measuring problem solving Preformance
The effectiveness of a search technique can be measured in at least three ways.
1) Does it find a solution?
2) Is it a good solution (one with a low path cost)?
3) What is the search cost associated with the ...

When we talk of problem solving in what follows, we will usually suppose that all the problems to be solved are initially well-defined.

Heitkotter & Beasley (2000) elaborate: 'Evolutionary algorithm is an umbrella term used to describe computer-based problem solving systems which use computational models of some of the known mechanisms of evolution as key elements in their design and ...

In other words, object-oriented problem solving consists of designing objects whose individual behaviors, and interactions solve a specific problem.

As you already know from the chapter about search space, problem solving can be often expressed as looking for extreme of a function. This is exactly what the problem shown here is. Some function is given and GA tries to find minimum of the function.

PRODIGY is a system that integrates problem solving, planning, and learning methods in a single architecture.

Greedy algorithms are algorithms which follow the problem solving meta-heuristic of making the locally optimum choice at each stage with the hope of finding the global optimum.

As you can see from a few of the previous comments, some people have had no problem solving it, instead some have had even faster rates, and even more creative ways to do it! ...

Success in problem solving by humans and by AI programs seems to rely on properties of problems and problem solving methods that the neither the complexity researchers nor the AI community have been able to identify precisely.

Look to subjects that encourage problem solving, modelling and undersatnding other people's skills and their roles. Computing, Maths (especially Logic), English (especially Grammar), Psychology are topics that I would suggest.

The field of AI has spawned many new problem solving and search techniques that are in common use.
Some medical diagnostics appliances have been created using AI techniques ...

constraint-based reasoning problem solving based on reasoning about given constraints and propagating constraints to narrow the range of possible solutions.

Weak AI In contrast to strong AI, weak AI refers to the use of software to study or accomplish specific problem solving or reasoning tasks that do not encompass (or in some cases, are completely outside of) the full range of human ...

However, with CBR it is not necessary to elicit Expert Knowledge but it is essential that a comprehensive data base of past problem solving examples is available.

When downsized data miners develop software, the end product is usually a complex tool (as opposed to a problem solving application) or intermediate software product.

through sensory means and the ability to make decisions in unforeseen circumstances without human intervention. Typical areas of research in AI include game playing, natural language understanding and synthesis, computer vision, problem solving, ...

See also: Artificial intelligence, Knowledge, AI, Agent, Planning