2,863 research outputs found
Constructing Conditional Plans by a Theorem-Prover
The research on conditional planning rejects the assumptions that there is no
uncertainty or incompleteness of knowledge with respect to the state and
changes of the system the plans operate on. Without these assumptions the
sequences of operations that achieve the goals depend on the initial state and
the outcomes of nondeterministic changes in the system. This setting raises the
questions of how to represent the plans and how to perform plan search. The
answers are quite different from those in the simpler classical framework. In
this paper, we approach conditional planning from a new viewpoint that is
motivated by the use of satisfiability algorithms in classical planning.
Translating conditional planning to formulae in the propositional logic is not
feasible because of inherent computational limitations. Instead, we translate
conditional planning to quantified Boolean formulae. We discuss three
formalizations of conditional planning as quantified Boolean formulae, and
present experimental results obtained with a theorem-prover
Scalable Verification of Markov Decision Processes
Markov decision processes (MDP) are useful to model concurrent process
optimisation problems, but verifying them with numerical methods is often
intractable. Existing approximative approaches do not scale well and are
limited to memoryless schedulers. Here we present the basis of scalable
verification for MDPSs, using an O(1) memory representation of
history-dependent schedulers. We thus facilitate scalable learning techniques
and the use of massively parallel verification.Comment: V4: FMDS version, 12 pages, 4 figure
Abductive and Consistency-Based Diagnosis Revisited: a Modeling Perspective
Diagnostic reasoning has been characterized logically as consistency-based
reasoning or abductive reasoning. Previous analyses in the literature have
shown, on the one hand, that choosing the (in general more restrictive)
abductive definition may be appropriate or not, depending on the content of the
knowledge base [Console&Torasso91], and, on the other hand, that, depending on
the choice of the definition the same knowledge should be expressed in
different form [Poole94].
Since in Model-Based Diagnosis a major problem is finding the right way of
abstracting the behavior of the system to be modeled, this paper discusses the
relation between modeling, and in particular abstraction in the model, and the
notion of diagnosis.Comment: 5 pages, 8th Int. Workshop on Nonmonotonic Reasoning, 200
Some Remarks about the Complexity of Epidemics Management
Recent outbreaks of Ebola, H1N1 and other infectious diseases have shown that
the assumptions underlying the established theory of epidemics management are
too idealistic. For an improvement of procedures and organizations involved in
fighting epidemics, extended models of epidemics management are required. The
necessary extensions consist in a representation of the management loop and the
potential frictions influencing the loop. The effects of the non-deterministic
frictions can be taken into account by including the measures of robustness and
risk in the assessment of management options. Thus, besides of the increased
structural complexity resulting from the model extensions, the computational
complexity of the task of epidemics management - interpreted as an optimization
problem - is increased as well. This is a serious obstacle for analyzing the
model and may require an additional pre-processing enabling a simplification of
the analysis process. The paper closes with an outlook discussing some
forthcoming problems
Smart Sampling for Lightweight Verification of Markov Decision Processes
Markov decision processes (MDP) are useful to model optimisation problems in
concurrent systems. To verify MDPs with efficient Monte Carlo techniques
requires that their nondeterminism be resolved by a scheduler. Recent work has
introduced the elements of lightweight techniques to sample directly from
scheduler space, but finding optimal schedulers by simple sampling may be
inefficient. Here we describe "smart" sampling algorithms that can make
substantial improvements in performance.Comment: IEEE conference style, 11 pages, 5 algorithms, 11 figures, 1 tabl
Logic-Based Specification Languages for Intelligent Software Agents
The research field of Agent-Oriented Software Engineering (AOSE) aims to find
abstractions, languages, methodologies and toolkits for modeling, verifying,
validating and prototyping complex applications conceptualized as Multiagent
Systems (MASs). A very lively research sub-field studies how formal methods can
be used for AOSE. This paper presents a detailed survey of six logic-based
executable agent specification languages that have been chosen for their
potential to be integrated in our ARPEGGIO project, an open framework for
specifying and prototyping a MAS. The six languages are ConGoLog, Agent-0, the
IMPACT agent programming language, DyLog, Concurrent METATEM and Ehhf. For each
executable language, the logic foundations are described and an example of use
is shown. A comparison of the six languages and a survey of similar approaches
complete the paper, together with considerations of the advantages of using
logic-based languages in MAS modeling and prototyping.Comment: 67 pages, 1 table, 1 figure. Accepted for publication by the Journal
"Theory and Practice of Logic Programming", volume 4, Maurice Bruynooghe
Editor-in-Chie
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