10,418 research outputs found
A canonical theory of dynamic decision-making
Decision-making behavior is studied in many very different fields, from medicine and eco- nomics to psychology and neuroscience, with major contributions from mathematics and statistics, computer science, AI, and other technical disciplines. However the conceptual- ization of what decision-making is and methods for studying it vary greatly and this has resulted in fragmentation of the field. A theory that can accommodate various perspectives may facilitate interdisciplinary working. We present such a theory in which decision-making is articulated as a set of canonical functions that are sufficiently general to accommodate diverse viewpoints, yet sufficiently precise that they can be instantiated in different ways for specific theoretical or practical purposes. The canons cover the whole decision cycle, from the framing of a decision based on the goals, beliefs, and background knowledge of the decision-maker to the formulation of decision options, establishing preferences over them, and making commitments. Commitments can lead to the initiation of new decisions and any step in the cycle can incorporate reasoning about previous decisions and the rationales for them, and lead to revising or abandoning existing commitments. The theory situates decision-making with respect to other high-level cognitive capabilities like problem solving, planning, and collaborative decision-making. The canonical approach is assessed in three domains: cognitive and neuropsychology, artificial intelligence, and decision engineering
Preservation of Semantic Properties during the Aggregation of Abstract Argumentation Frameworks
An abstract argumentation framework can be used to model the argumentative
stance of an agent at a high level of abstraction, by indicating for every pair
of arguments that is being considered in a debate whether the first attacks the
second. When modelling a group of agents engaged in a debate, we may wish to
aggregate their individual argumentation frameworks to obtain a single such
framework that reflects the consensus of the group. Even when agents disagree
on many details, there may well be high-level agreement on important semantic
properties, such as the acceptability of a given argument. Using techniques
from social choice theory, we analyse under what circumstances such semantic
properties agreed upon by the individual agents can be preserved under
aggregation.Comment: In Proceedings TARK 2017, arXiv:1707.0825
A Framework for Combining Defeasible Argumentation with Labeled Deduction
In the last years, there has been an increasing demand of a variety of
logical systems, prompted mostly by applications of logic in AI and other
related areas. Labeled Deductive Systems (LDS) were developed as a flexible
methodology to formalize such a kind of complex logical systems. Defeasible
argumentation has proven to be a successful approach to formalizing commonsense
reasoning, encompassing many other alternative formalisms for defeasible
reasoning. Argument-based frameworks share some common notions (such as the
concept of argument, defeater, etc.) along with a number of particular features
which make it difficult to compare them with each other from a logical
viewpoint. This paper introduces LDSar, a LDS for defeasible argumentation in
which many important issues concerning defeasible argumentation are captured
within a unified logical framework. We also discuss some logical properties and
extensions that emerge from the proposed framework.Comment: 15 pages, presented at CMSRA Workshop 2003. Buenos Aires, Argentin
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