63,779 research outputs found
AAAI 2008 Workshop Reports
AAAI was pleased to present the AAAI-08 Workshop Program, held Sunday and Monday, July 13-14, in Chicago, Illinois, USA. The program included the following 15 workshops: Advancements in POMDP Solvers; AI Education Workshop Colloquium; Coordination, Organizations, Institutions, and Norms in Agent Systems, Enhanced Messaging; Human Implications of Human-Robot Interaction; Intelligent Techniques for Web Personalization and Recommender Systems; Metareasoning: Thinking about Thinking; Multidisciplinary Workshop on Advances in Preference Handling; Search in Artificial Intelligence and Robotics; Spatial and Temporal Reasoning; Trading Agent Design and Analysis; Transfer Learning for Complex Tasks; What Went Wrong and Why: Lessons from AI Research and Applications; and Wikipedia and Artificial Intelligence: An Evolving Synergy
Preferences in Case-Based Reasoning
Case-based reasoning (CBR) is a well-established problem solving paradigm
that has been used in a wide range of real-world applications. Despite
its great practical success, work on the theoretical foundations of CBR is
still under way, and a coherent and universally applicable methodological
framework is yet missing. The absence of such a framework inspired the
motivation for the work developed in this thesis. Drawing on recent research
on preference handling in Artificial Intelligence and related fields, the goal of
this work is to develop a well theoretically-founded framework on the basis
of formal concepts and methods for knowledge representation and reasoning
with preferences
Properties of ABA+ for Non-Monotonic Reasoning
We investigate properties of ABA+, a formalism that extends the well studied
structured argumentation formalism Assumption-Based Argumentation (ABA) with a
preference handling mechanism. In particular, we establish desirable properties
that ABA+ semantics exhibit. These pave way to the satisfaction by ABA+ of some
(arguably) desirable principles of preference handling in argumentation and
nonmonotonic reasoning, as well as non-monotonic inference properties of ABA+
under various semantics.Comment: This is a revised version of the paper presented at the worksho
Building Ethically Bounded AI
The more AI agents are deployed in scenarios with possibly unexpected
situations, the more they need to be flexible, adaptive, and creative in
achieving the goal we have given them. Thus, a certain level of freedom to
choose the best path to the goal is inherent in making AI robust and flexible
enough. At the same time, however, the pervasive deployment of AI in our life,
whether AI is autonomous or collaborating with humans, raises several ethical
challenges. AI agents should be aware and follow appropriate ethical principles
and should thus exhibit properties such as fairness or other virtues. These
ethical principles should define the boundaries of AI's freedom and creativity.
However, it is still a challenge to understand how to specify and reason with
ethical boundaries in AI agents and how to combine them appropriately with
subjective preferences and goal specifications. Some initial attempts employ
either a data-driven example-based approach for both, or a symbolic rule-based
approach for both. We envision a modular approach where any AI technique can be
used for any of these essential ingredients in decision making or decision
support systems, paired with a contextual approach to define their combination
and relative weight. In a world where neither humans nor AI systems work in
isolation, but are tightly interconnected, e.g., the Internet of Things, we
also envision a compositional approach to building ethically bounded AI, where
the ethical properties of each component can be fruitfully exploited to derive
those of the overall system. In this paper we define and motivate the notion of
ethically-bounded AI, we describe two concrete examples, and we outline some
outstanding challenges.Comment: Published at AAAI Blue Sky Track, winner of Blue Sky Awar
Logic Programs with Compiled Preferences
We describe an approach for compiling preferences into logic programs under
the answer set semantics. An ordered logic program is an extended logic program
in which rules are named by unique terms, and in which preferences among rules
are given by a set of dedicated atoms. An ordered logic program is transformed
into a second, regular, extended logic program wherein the preferences are
respected, in that the answer sets obtained in the transformed theory
correspond with the preferred answer sets of the original theory. Our approach
allows both the specification of static orderings (as found in most previous
work), in which preferences are external to a logic program, as well as
orderings on sets of rules. In large part then, we are interested in describing
a general methodology for uniformly incorporating preference information in a
logic program. Since the result of our translation is an extended logic
program, we can make use of existing implementations, such as dlv and smodels.
To this end, we have developed a compiler, available on the web, as a front-end
for these programming systems
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