Discuss the relationship between data, information, and knowledge. Support your discussion with at least 3 academically reviewed articles.

Reasoning with Imperfect Information and Knowledge

Gabriele Kern-Isberner1 • Igor Douven2 •

Markus Knauff3 • Henri Prade4

Published online: 21 March 2017

� Springer Science+Business Media Dordrecht 2017

Nowadays, smart and intelligent computer systems are part of our everyday lives.

However, computational intelligence is rather different from human intelligence, in

particular, the problem of reasoning with uncertain, imprecise, incomplete, or

inconsistent (in short, imperfect) information often renders computational methods

relying on strict or deductive logics obsolete or fallacious whereas humans cope

with this problem with relative ease. Reasoning with imperfect information plays a

central role in practical deliberation and rational decision making. Indeed, models of

human context-dependent reasoning that synthesise logical, philosophical and

psychological aspects would provide novel insights into rational human reasoning.

Several of such formal approaches have been developed in philosophy and artificial

intelligence (AI) over the past decades, and an increasing interest in these new

formal methods for rational human reasoning under uncertainty have emerged in

psychology. Likewise, philosophers and computer scientists have shown an

increased attention to the experimental methods of psychology recently. In

particular for computer scientists and AI researchers, it is becoming more and

& Gabriele Kern-Isberner [email protected]

Igor Douven

[email protected]

Markus Knauff

[email protected]

Henri Prade

[email protected]

1 Technische Universität Dortmund, Dortmund, Germany

2 Paris-Sorbonne Université, Paris, France

3 Justus-Liebig-Universität Gießen, Gießen, Germany

4 Université Paul Sabatier, Toulouse, France

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Minds & Machines (2017) 27:7–9

DOI 10.1007/s11023-017-9427-4

more interesting to see whether the systems they have been developing are

cognitively, respectively descriptively adequate. A synthesis of rational reasoning

with imperfect information that takes into account research done in artificial

intelligence, psychology and philosophy is needed for providing a clearer view of

where we are and what are the pending issues both from computational resp. logical

and cognitive viewpoints. This will give new impetus to formal, cognitive, and

empirical models of rational reasoning, and as a side effect, might help making

intelligent systems more helpful for their human users.

The idea for this special issue emerged from the seminar 15221: Multi-

disciplinary approaches to reasoning with imperfect information and knowledge—A

synthesis and a roadmap of challenges which took place at Schloss Dagstuhl,

Leibniz Center for Computer Science, May 26th–29th, 2015, and was organized by

the editors of this special issue (for a report on this seminar, please see http://drops.

dagstuhl.de/opus/frontdoor.php?source_opus=5403). This seminar brought together

researchers interested in rational and uncertain reasoning from a very broad sci-

entific scope to present and discuss problems and approaches from different disci-

plines, consolidate common grounds, and initiate new interdisciplinary

collaborations. It took profit from the fact that computer scientists, philosophers, and

psychologists have started quite recently to work in a common methodological

paradigm with overlapping goals, converging interests, and largely shared research

tools. The attendees identified challenges for new paradigms of rational reasoning,

and discussed visions and foci for more interdisciplinary work. Most of the papers

contained in this special issue were contributed by participants of this seminar and

their colleagues.

Illusions in reasoning by Sangeet S. Khemlani and P.N. Johnson-Laird aims to

show that human reasoners commit systematic fallacies, and that the theory of

mental models can predict these errors. In particular, they point out that such

fallacies are caused by the reasoners focussing on what is true in a possibility, but

not what is false.

Uncertain reasoning is a core topic of this special issue, and probability theory is

often considered to be the best formal tool for that. However, Tarek R. Besold, Artur

d’Avila Garcez, Keith Stenning, Leendert van der Torre, and Michiel van

Lambalgen make a claim for non-probabilistic methods in Reasoning in non-

probabilistic uncertainty: logic programming and neural-symbolic computing as

examples. They describe and emphasise the benefits of logic-based methods like

logic programming under Kleene semantics and a neural-symbolic implementation

of Input/Output logic.

The high quality of non-probabilistic, even non-quantitative approaches to

default logics for modelling human reasoning is also the topic of Formal

nonmonotonic theories and properties of human defeasible reasoning by Marco

Ragni, Christian Eichhorn, Tanja Bock, Gabriele Kern-Isberner, and Ping Ping Tse.

The authors focus on conditionals as crucial entities of human reasoning and

machine intelligence. From both a formal and an empirical perspective, they

analyze systematically the power of formal nonmonotonic systems to model

possible explicit defeaters, as in the Suppression Task, and more implicit

conditional rules that trigger nonmonotonic reasoning by keywords.

8 G. Kern-Isberner et al.

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Niki Pfeifer and Leena Tulkki also deal with conditionals in their paper

Conditionals, counterfactuals, and rational reasoning. An experimental study on

basic principles. They present a unified approach for investigating rational

reasoning about basic argument forms involving different forms of conditionals

within coherence-based probability logic. In particular, they report on a new

experiment showing that people interpret indicative conditionals and counterfac-

tuals by coherent conditional probability assertions, and that they negate

conditionals by negating their consequents.

In Stability and scepticism in the modelling of doxastic states: probabilities and

plain beliefs, Hans Rott discusses two recent accounts of bridging the gap between

plain logical belief and belief based on subjective probability, namely the Stability

Theory of Hannes Leitgeb (2013–2015), and the Probalogical Theory (or Tracking

Theory) of Hanti Lin and Kevin Kelly (2012), and he points out problems with these

approaches.

Didier Dubois, Emiliano Lorini, Henri Prade present and discuss a logic of

desires in The strength of desires: a logical approach. While there are some

similarities to beliefs from a formal point of view, the authors argue that the way

desires should be treated is very different from beliefs. They also consider revision

of desires and again, point out parallels to and differences from belief revision. They

suggest possibility theory as a suitable syntactic framework for encoding desire

change.

Finally, Pierre Bisquert, Madalina Croitoru, Florence Dupin de Saint Cyr, and

Abdelraouf Hecham address the benefits of argumentation theories in their paper

Formalizing cognitive acceptance of arguments: Durum wheat selection interdis-

ciplinary study. They propose a computational cognitive model for argument

acceptance which is based on the dual model system in cognitive psychology, and

apply this to an interdisciplinary project in an agronomy setting.

Acknowledgements First of all, we would like to thank Schloss Dagstuhl for hosting our seminar, providing us with an excellent service and the opportunity for having this scientific come together in the

unique collaborative and inspiring atmosphere of the castle. Without this seminar, this special issue would

not have been possible, and we are also grateful to all attendees of the seminar for sharing their ideas with

us. The idea for this seminar arose from activities of the DFG Priority Program 1516 New frameworks of

rationality which was initiated by Markus Knauff; the pleasure we have been experiencing with

interdisciplinary work and fruitful collaborations within this Priority Program have encouraged us to

discuss our research work and future goals with a broader audience. We are also thankful to all authors of

this issue who provided with their papers a proof that the Dagstuhl seminar was not just one inspiring

week but also had long-lasting effects. Gregory Wheeler was one of the participants of that seminar, and

he was the first to have the idea of this special issue, so our special thanks go to him, and also to the

current editor-in-chief Mariarosaria Taddeo whose help with finalizing this issue is very much

appreciated. We also thank all reviewers of the submitted papers for delivering reviews of an excellent

quality in time.

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