115,358 research outputs found
Adaptive Parallel Iterative Deepening Search
Many of the artificial intelligence techniques developed to date rely on
heuristic search through large spaces. Unfortunately, the size of these spaces
and the corresponding computational effort reduce the applicability of
otherwise novel and effective algorithms. A number of parallel and distributed
approaches to search have considerably improved the performance of the search
process. Our goal is to develop an architecture that automatically selects
parallel search strategies for optimal performance on a variety of search
problems. In this paper we describe one such architecture realized in the
Eureka system, which combines the benefits of many different approaches to
parallel heuristic search. Through empirical and theoretical analyses we
observe that features of the problem space directly affect the choice of
optimal parallel search strategy. We then employ machine learning techniques to
select the optimal parallel search strategy for a given problem space. When a
new search task is input to the system, Eureka uses features describing the
search space and the chosen architecture to automatically select the
appropriate search strategy. Eureka has been tested on a MIMD parallel
processor, a distributed network of workstations, and a single workstation
using multithreading. Results generated from fifteen puzzle problems, robot arm
motion problems, artificial search spaces, and planning problems indicate that
Eureka outperforms any of the tested strategies used exclusively for all
problem instances and is able to greatly reduce the search time for these
applications
Narrative based Postdictive Reasoning for Cognitive Robotics
Making sense of incomplete and conflicting narrative knowledge in the
presence of abnormalities, unobservable processes, and other real world
considerations is a challenge and crucial requirement for cognitive robotics
systems. An added challenge, even when suitably specialised action languages
and reasoning systems exist, is practical integration and application within
large-scale robot control frameworks.
In the backdrop of an autonomous wheelchair robot control task, we report on
application-driven work to realise postdiction triggered abnormality detection
and re-planning for real-time robot control: (a) Narrative-based knowledge
about the environment is obtained via a larger smart environment framework; and
(b) abnormalities are postdicted from stable-models of an answer-set program
corresponding to the robot's epistemic model. The overall reasoning is
performed in the context of an approximate epistemic action theory based
planner implemented via a translation to answer-set programming.Comment: Commonsense Reasoning Symposium, Ayia Napa, Cyprus, 201
Efficient Bayesian Planning
Artificial Intelligence (AI) is a long-studied and yet very active field of research. The list of things differentiating humans from AI grows thinner but the dream of an artificial general intelligence remains elusive. Sequential Decision Making is a subfield of AI that poses a seemingly benign question ``How to act optimally in an unknown environment?\u27\u27. This requires the AI agent to learn about its environment as well as plan an action sequence given its current knowledge about it. The two common problem settings are partial observability and unknown environment dynamics. Bayesian planning deals with these issues by simultaneously defining a single planning problem which considers the simultaneous effects of an action on both learning and goal search. The technique involves dealing with infinite tree data structures which are hard to store but essential for computing the optimal plan. Finally, we consider the minimax setting where the Bayesian prior is chosen by an adversary and therefore a worst case policy needs to be found.In this thesis, we present novel Bayesian planning algorithms. First, we propose DSS (Deeper, Sparser Sampling) for the case of unknown environment dynamics. It is a meta-algorithm derived from a simple insight about the Bayes rule, which beats the state-of-the-art across the board from discrete to continuous state settings. A theoretical analysis provides a high probability bound on its performance. Our analysis is different from previous approaches in the literature in terms of problem formulation and formal guarantees. The result also contrasts with those of previous comparable BRL algorithms, which typically provide asymptotic convergence guarantees. Suitable Bayesian models and their corresponding planners are proposed for implementing the discrete and continuous versions of DSS. We then address the issue of partial observability via our second algorithm, FMP (Finite Memory Planner). This uses depth-dependent partitioning of the infinite planning tree. Experimental results demonstrate comparable performance to the current state-of-the-art for both discrete and continuous settings. Finally, we propose algorithms for finding the best policy for the worst case belief in the Minimax Bayesian setting
Online algorithms for POMDPs with continuous state, action, and observation spaces
Online solvers for partially observable Markov decision processes have been
applied to problems with large discrete state spaces, but continuous state,
action, and observation spaces remain a challenge. This paper begins by
investigating double progressive widening (DPW) as a solution to this
challenge. However, we prove that this modification alone is not sufficient
because the belief representations in the search tree collapse to a single
particle causing the algorithm to converge to a policy that is suboptimal
regardless of the computation time. This paper proposes and evaluates two new
algorithms, POMCPOW and PFT-DPW, that overcome this deficiency by using
weighted particle filtering. Simulation results show that these modifications
allow the algorithms to be successful where previous approaches fail.Comment: Added Multilane sectio
Sampling-Based Query Re-Optimization
Despite of decades of work, query optimizers still make mistakes on
"difficult" queries because of bad cardinality estimates, often due to the
interaction of multiple predicates and correlations in the data. In this paper,
we propose a low-cost post-processing step that can take a plan produced by the
optimizer, detect when it is likely to have made such a mistake, and take steps
to fix it. Specifically, our solution is a sampling-based iterative procedure
that requires almost no changes to the original query optimizer or query
evaluation mechanism of the system. We show that this indeed imposes low
overhead and catches cases where three widely used optimizers (PostgreSQL and
two commercial systems) make large errors.Comment: This is the extended version of a paper with the same title and
authors that appears in the Proceedings of the ACM SIGMOD International
Conference on Management of Data (SIGMOD 2016
A Formal Framework for Speedup Learning from Problems and Solutions
Speedup learning seeks to improve the computational efficiency of problem
solving with experience. In this paper, we develop a formal framework for
learning efficient problem solving from random problems and their solutions. We
apply this framework to two different representations of learned knowledge,
namely control rules and macro-operators, and prove theorems that identify
sufficient conditions for learning in each representation. Our proofs are
constructive in that they are accompanied with learning algorithms. Our
framework captures both empirical and explanation-based speedup learning in a
unified fashion. We illustrate our framework with implementations in two
domains: symbolic integration and Eight Puzzle. This work integrates many
strands of experimental and theoretical work in machine learning, including
empirical learning of control rules, macro-operator learning, Explanation-Based
Learning (EBL), and Probably Approximately Correct (PAC) Learning.Comment: See http://www.jair.org/ for any accompanying file
Action planning for graph transition systems
Graphs are suitable modeling formalisms for software and hardware systems involving aspects such as communication,
object orientation, concurrency, mobility and distribution. State spaces of such systems can be represented by graph transition systems, which are basically transition systems whose states and transitions represent graphs and graph morphisms. In this paper, we propose the modeling of graph transition systems in PDDL and the application of heuristic search planning for their analysis. We consider different heuristics and present experimental results
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