20,420 research outputs found
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
Great Expectations. Part II: Generalized Expected Utility as a Universal Decision Rule
Many different rules for decision making have been introduced in the
literature. We show that a notion of generalized expected utility proposed in
Part I of this paper is a universal decision rule, in the sense that it can
represent essentially all other decision rules.Comment: Preliminary version appears in Proc. 18th International Joint
Conference on AI (IJCAI), 2003, pp. 297-30
An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise
Collaborative filtering based recommender systems have proven to be extremely
successful in settings where user preference data on items is abundant.
However, collaborative filtering algorithms are hindered by their weakness
against the item cold-start problem and general lack of interpretability.
Ontology-based recommender systems exploit hierarchical organizations of users
and items to enhance browsing, recommendation, and profile construction. While
ontology-based approaches address the shortcomings of their collaborative
filtering counterparts, ontological organizations of items can be difficult to
obtain for items that mostly belong to the same category (e.g., television
series episodes). In this paper, we present an ontology-based recommender
system that integrates the knowledge represented in a large ontology of
literary themes to produce fiction content recommendations. The main novelty of
this work is an ontology-based method for computing similarities between items
and its integration with the classical Item-KNN (K-nearest neighbors)
algorithm. As a study case, we evaluated the proposed method against other
approaches by performing the classical rating prediction task on a collection
of Star Trek television series episodes in an item cold-start scenario. This
transverse evaluation provides insights into the utility of different
information resources and methods for the initial stages of recommender system
development. We found our proposed method to be a convenient alternative to
collaborative filtering approaches for collections of mostly similar items,
particularly when other content-based approaches are not applicable or
otherwise unavailable. Aside from the new methods, this paper contributes a
testbed for future research and an online framework to collaboratively extend
the ontology of literary themes to cover other narrative content.Comment: 25 pages, 6 figures, 5 tables, minor revision
Rational Verification in Iterated Electric Boolean Games
Electric boolean games are compact representations of games where the players
have qualitative objectives described by LTL formulae and have limited
resources. We study the complexity of several decision problems related to the
analysis of rationality in electric boolean games with LTL objectives. In
particular, we report that the problem of deciding whether a profile is a Nash
equilibrium in an iterated electric boolean game is no harder than in iterated
boolean games without resource bounds. We show that it is a PSPACE-complete
problem. As a corollary, we obtain that both rational elimination and rational
construction of Nash equilibria by a supervising authority are PSPACE-complete
problems.Comment: In Proceedings SR 2016, arXiv:1607.0269
Possibilistic Boolean games: strategic reasoning under incomplete information
Boolean games offer a compact alternative to normal-form games, by encoding the goal of each agent as a propositional formula. In this paper, we show how this framework can be naturally extended to model situations in which agents are uncertain about other agents' goals. We first use uncertainty measures from possibility theory to semantically define (solution concepts to) Boolean games with incomplete information. Then we present a syntactic characterization of these semantics, which can readily be implemented, and we characterize the computational complexity
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