7,570 research outputs found
FLECS: Planning with a Flexible Commitment Strategy
There has been evidence that least-commitment planners can efficiently handle
planning problems that involve difficult goal interactions. This evidence has
led to the common belief that delayed-commitment is the "best" possible
planning strategy. However, we recently found evidence that eager-commitment
planners can handle a variety of planning problems more efficiently, in
particular those with difficult operator choices. Resigned to the futility of
trying to find a universally successful planning strategy, we devised a planner
that can be used to study which domains and problems are best for which
planning strategies. In this article we introduce this new planning algorithm,
FLECS, which uses a FLExible Commitment Strategy with respect to plan-step
orderings. It is able to use any strategy from delayed-commitment to
eager-commitment. The combination of delayed and eager operator-ordering
commitments allows FLECS to take advantage of the benefits of explicitly using
a simulated execution state and reasoning about planning constraints. FLECS can
vary its commitment strategy across different problems and domains, and also
during the course of a single planning problem. FLECS represents a novel
contribution to planning in that it explicitly provides the choice of which
commitment strategy to use while planning. FLECS provides a framework to
investigate the mapping from planning domains and problems to efficient
planning strategies.Comment: See http://www.jair.org/ for an online appendix and other files
accompanying this articl
On Graph Refutation for Relational Inclusions
We introduce a graphical refutation calculus for relational inclusions: it
reduces establishing a relational inclusion to establishing that a graph
constructed from it has empty extension. This sound and complete calculus is
conceptually simpler and easier to use than the usual ones.Comment: In Proceedings LSFA 2011, arXiv:1203.542
On Graphical Calculi for Modal Logics
We present a graphical approach to classical and intuitionistic modal logics, which provides uniform formalisms for expressing, analysing and comparing their semantics. This approach uses the flexibility of graphical calculi to express directly and intuitively the semantics for modal logics. We illustrate the benefits of these ideas by applying them to some familiar cases of classical and intuitionistic multi-modal logics.CĂĄlculos GrĂĄficos para lĂłgicas modais
Apresentamos uma abordagem grĂĄfica para as lĂłgicas modais clĂĄssica e intuicionista, capaz de fornecer formalismos uniformes para expressar, analisar e comparar suas respectivas semânticas. Tal abordagem utiliza a flexibilidade dos cĂĄlculos grĂĄficos para expressar, direta e intuitivamente, a semântica das lĂłgicas modais. Ilustramos os benefĂcios dessas ideias aplicando-as a alguns casos conhecidos de lĂłgicas multimodais clĂĄssica e intuicionista.---Artigo em inglĂŞs
Existence of Multiagent Equilibria with Limited Agents
Multiagent learning is a necessary yet challenging problem as multiagent
systems become more prevalent and environments become more dynamic. Much of the
groundbreaking work in this area draws on notable results from game theory, in
particular, the concept of Nash equilibria. Learners that directly learn an
equilibrium obviously rely on their existence. Learners that instead seek to
play optimally with respect to the other players also depend upon equilibria
since equilibria are fixed points for learning. From another perspective,
agents with limitations are real and common. These may be undesired physical
limitations as well as self-imposed rational limitations, such as abstraction
and approximation techniques, used to make learning tractable. This article
explores the interactions of these two important concepts: equilibria and
limitations in learning. We introduce the question of whether equilibria
continue to exist when agents have limitations. We look at the general effects
limitations can have on agent behavior, and define a natural extension of
equilibria that accounts for these limitations. Using this formalization, we
make three major contributions: (i) a counterexample for the general existence
of equilibria with limitations, (ii) sufficient conditions on limitations that
preserve their existence, (iii) three general classes of games and limitations
that satisfy these conditions. We then present empirical results from a
specific multiagent learning algorithm applied to a specific instance of
limited agents. These results demonstrate that learning with limitations is
feasible, when the conditions outlined by our theoretical analysis hold
ASAP: An Automatic Algorithm Selection Approach for Planning
Despite the advances made in the last decade in automated planning, no planner out-
performs all the others in every known benchmark domain. This observation motivates
the idea of selecting different planning algorithms for different domains. Moreover, the
plannersâ performances are affected by the structure of the search space, which depends
on the encoding of the considered domain. In many domains, the performance of a plan-
ner can be improved by exploiting additional knowledge, for instance, in the form of
macro-operators or entanglements.
In this paper we propose ASAP, an automatic Algorithm Selection Approach for
Planning that: (i) for a given domain initially learns additional knowledge, in the form
of macro-operators and entanglements, which is used for creating different encodings
of the given planning domain and problems, and (ii) explores the 2 dimensional space
of available algorithms, defined as encodingsâplanners couples, and then (iii) selects the
most promising algorithm for optimising either the runtimes or the quality of the solution
plans
- âŚ