Premises => Algorithms => Conclusions. â Example : ... The conclusion always derives from the premises by logical operations but this does not ... Another application are the « expert systems » : in order to choose the ... Research state : information space containing all data and procedures that will be used to solve the ...
Human-computer interaction Chapter 3 – Reasoning and problem solving
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Definitions ●
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Reasoning –
The process by which we use the knowledge we have to draw conclusions or infer something new about the subject of interest
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Types of reasoning ●
Deductive
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Inductive
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Abductive
Problem solving –
Use information we have to find solutions in new situations ●
Gestalt (or form) theory
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The problem space (Newell et Simon)
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Analogy
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Deductive reasoning ●
Premises => Algorithms => Conclusions –
Example :
We don't work on weekends. We are saturday Saturday is the first day of the weekend Conclusion : I do not work today –
The conclusion always derives from the premises by logical operations but this does not mean that the premises were correct !
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Deductive reasoning may lead to false conclusions
Certain people are babies Certain babies cry conclusion : certain people cry ??? FALSE, since it is not said that ALL babies are people... those who cry may not be people Interaction homme-ordinateur
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Inductive reasoning (1) ●
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Inductive reasoning corresponds to the generalization that we make from cases we know to infer information about case we don't know. The facts are true (experience) but the generalizations (making a law) may not be : one counterexample is enough to disprove the « law ». Example 1 : –
The first line hereafter shows 5 hexagones ; it is asked to add the 6-th one, selected between those shown in the second line.
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Inductive reasoning (2)
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Inductive reasoning (2) ●
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Inductive reasoning helps us learn and orient in a changing environment. But the law established by inductive reasoning must be constantly verified Example 2 : Wason cards
7 E 4 K Each card has a number on one side and a letter on the other side. Find which cards must be turned on to prouve the following « law » that was « established » by previous tests, not presented here : If a card has a vowell on one side, it has an even number on the other side What happens if we omit saying that each card has a vowell on one side and a number on the other ?
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Abductive reasoning ●
Abduction : it is the type of reasoning used to determine the causes of a given effect : –
The result is observed
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One knows causes to effect possible relations
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A hypothesis to explain the causes is put forward
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The hypothesis is checked
This tipe of reasoning is common to police inquieries and to medical diagnostic, but it is also used in everyday life. Another application are the « expert systems » : in order to choose the most appropriate hypothesis, one sets up a mechanism to evaluate the « value » of each one proposed.
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Problem solving : gestalt theory ●
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Gestalten in german means « to structure, to give form » ; it has no equivalent in other languages and for this reason the word is used « as is » in french, english, romanian... The main ideas are : –
We are pre-programmed to percieve forms that we use constantly to understand our environment, by grouping and restructuring them to solve the new problems
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The « whole » is not the sum of the parts : we percieve the whole before percieving the details
This principles are the basis for the « gestalt » theory and are also useful guides to HCI design. The theory establishes the following « laws » :
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The « laws » of the gestalt theory ●
The good form : an informal set is first percieved as a simple form, stable and symmetric
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Continuity : close points are first percieved as forming a line
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Similitude : in a set we try to assemble similar objects
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Common destiny : the objects moving simultanously in the same direction are percieved as being part of the same form Closure : it is easier to percieve closed forms surrounding a surface
Conclusion : we combine our perception with our knowledge and thus we add information which is absent in the stimuli
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Gestaltic perception ●
Example :
Although attractive, this theory leaves unexplained many aspects of the problem solving mechanisms. Interaction homme-ordinateur
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Problem solving : the problem space
Initial state Search
Desired state
Search Problem space (Newell, Simon - 1972) Problem space : ● Initial state : problem statement and known conditions ● Research state : information space containing all data and procedures that will be used to solve the problem ● Goal state : target description Limitations : ● The model works if both the initial and final states are well defined and well known. Sometimes this is impossible and the state definitions become a part of the problem, like in the case of functional specificatons in programming. Interaction homme-ordinateur
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Problem solving exercise Identify the goals and operators involved in the problem 'delete the second paragraph of the document' on a word processor. Now use a word processor to delete a paragraph and note your actions, goals and subgoals. How well did they match your earlier description?
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Problem solving : analogy
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The analogy is used to solve a new problem by looking for similitudes with known situations and events, for which the problem was solved. Example : a doctor has to blast a malignian tumour with very strong rays. But he knows that the surrounding healthy tissue will also be destroyed ; however, if he lessens the rays' intensity the healthy tissue will be preserved but the tumour will remain. How should the doctor proceed ?
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Problem solving : the following story can help ?
A greek general attacking an ennemy city had to avoid the main road, since the defenders could easily stop his soldiers. He decided to split his army in small groups which attacked arriving on several roads simultaneously. The manoeuvre was successfull.
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Skill acquisition Understanding the skill acquisition mechanisms became more and more important with the advent of computer and it's use in computer assisted training. The help systems integrated to computer applications are also addressing skill acquisition mechanisms. ●
Concept acquisition –
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Skill acquisition –
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Repetition's role
Experts vs novices behavour (chess ?)
Methods –
Structured acquisition
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Try and error
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Human errors Understanding human errors' reasons is important for decision taking processes. Some errors may have catastrophic effects. There are many reasons why humans make errors ; wrong man / machine interfaces can be a cause (the case of the Spitfire pilots) ●
Bad design
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Response time
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Incompetence
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Lack of attention or concentration –
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To many informations arriving simultaneously can divert the user
Context change –
Unexpected (example : a difference between the real behaviour of a device and it's description in the documentation or explanation during training)
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Loose preparation (example : a behavioural change is asked but the operator was not informed or incomplete information was provided).
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