5 research outputs found
Multi-Attribute Decision Making using Weighted Description Logics
We introduce a framework based on Description Logics, which can be used to encode and solve decision problems in terms of combining inference services in DL and utility theory to represent preferences of the agent. The novelty of the approach is that we consider ABoxes as alternatives and weighted concept and role assertions as preferences in terms of possible outcomes. We discuss a relevant use case to show the benefits of the approach from the decision theory point of view
Multi-attribute decision making with weighted description logics
We introduce a decision-theoretic framework based on Description Logics
(DLs), which can be used to encode and solve single stage multi-attribute decision problems. In particular, we consider the background knowledge as a DL
knowledge base where each attribute is represented by a concept, weighted by
a utility value which is asserted by the user. This yields a compact representation of preferences over attributes. Moreover, we represent choices as knowledge
base individuals, and induce a ranking via the aggregation of attributes that
they satisfy. We discuss the benefits of the approach from a decision theory
point of view. Furthermore, we introduce an implementation of the framework
as a Protégé plugin called uDecide. The plugin takes as input an ontology as
background knowledge, and returns the choices consistent with the user’s (the
knowledge base) preferences. We describe a use case with data from DBpedia.
We also provide empirical results for its performance in the size of the ontology
using the reasoner Konclude
CP-nets: A Tool for Representing and Reasoning withConditional Ceteris Paribus Preference Statements
Information about user preferences plays a key role in automated decision
making. In many domains it is desirable to assess such preferences in a
qualitative rather than quantitative way. In this paper, we propose a
qualitative graphical representation of preferences that reflects conditional
dependence and independence of preference statements under a ceteris paribus
(all else being equal) interpretation. Such a representation is often compact
and arguably quite natural in many circumstances. We provide a formal semantics
for this model, and describe how the structure of the network can be exploited
in several inference tasks, such as determining whether one outcome dominates
(is preferred to) another, ordering a set outcomes according to the preference
relation, and constructing the best outcome subject to available evidence