14,232 research outputs found
Answer Set Planning Under Action Costs
Recently, planning based on answer set programming has been proposed as an
approach towards realizing declarative planning systems. In this paper, we
present the language Kc, which extends the declarative planning language K by
action costs. Kc provides the notion of admissible and optimal plans, which are
plans whose overall action costs are within a given limit resp. minimum over
all plans (i.e., cheapest plans). As we demonstrate, this novel language allows
for expressing some nontrivial planning tasks in a declarative way.
Furthermore, it can be utilized for representing planning problems under other
optimality criteria, such as computing ``shortest'' plans (with the least
number of steps), and refinement combinations of cheapest and fastest plans. We
study complexity aspects of the language Kc and provide a transformation to
logic programs, such that planning problems are solved via answer set
programming. Furthermore, we report experimental results on selected problems.
Our experience is encouraging that answer set planning may be a valuable
approach to expressive planning systems in which intricate planning problems
can be naturally specified and solved
Algorithmic Randomness as Foundation of Inductive Reasoning and Artificial Intelligence
This article is a brief personal account of the past, present, and future of
algorithmic randomness, emphasizing its role in inductive inference and
artificial intelligence. It is written for a general audience interested in
science and philosophy. Intuitively, randomness is a lack of order or
predictability. If randomness is the opposite of determinism, then algorithmic
randomness is the opposite of computability. Besides many other things, these
concepts have been used to quantify Ockham's razor, solve the induction
problem, and define intelligence.Comment: 9 LaTeX page
Requirements Problem and Solution Concepts for Adaptive Systems Engineering, and their Relationship to Mathematical Optimisation, Decision Analysis, and Expected Utility Theory
Requirements Engineering (RE) focuses on eliciting, modelling, and analyzing
the requirements and environment of a system-to-be in order to design its
specification. The design of the specification, usually called the Requirements
Problem (RP), is a complex problem solving task, as it involves, for each new
system-to-be, the discovery and exploration of, and decision making in, new and
ill-defined problem and solution spaces. The default RP in RE is to design a
specification of the system-to-be which (i) is consistent with given
requirements and conditions of its environment, and (ii) together with
environment conditions satisfies requirements. This paper (i) shows that the
Requirements Problem for Adaptive Systems (RPAS) is different from, and is not
a subclass of the default RP, (ii) gives a formal definition of RPAS, and (iii)
discusses implications for future research
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