1,817 research outputs found
Algorithms and Conditional Lower Bounds for Planning Problems
We consider planning problems for graphs, Markov decision processes (MDPs),
and games on graphs. While graphs represent the most basic planning model, MDPs
represent interaction with nature and games on graphs represent interaction
with an adversarial environment. We consider two planning problems where there
are k different target sets, and the problems are as follows: (a) the coverage
problem asks whether there is a plan for each individual target set, and (b)
the sequential target reachability problem asks whether the targets can be
reached in sequence. For the coverage problem, we present a linear-time
algorithm for graphs and quadratic conditional lower bound for MDPs and games
on graphs. For the sequential target problem, we present a linear-time
algorithm for graphs, a sub-quadratic algorithm for MDPs, and a quadratic
conditional lower bound for games on graphs. Our results with conditional lower
bounds establish (i) model-separation results showing that for the coverage
problem MDPs and games on graphs are harder than graphs and for the sequential
reachability problem games on graphs are harder than MDPs and graphs; (ii)
objective-separation results showing that for MDPs the coverage problem is
harder than the sequential target problem.Comment: Accepted at ICAPS'1
Weak Singular Hybrid Automata
The framework of Hybrid automata, introduced by Alur, Courcourbetis,
Henzinger, and Ho, provides a formal modeling and analysis environment to
analyze the interaction between the discrete and the continuous parts of
cyber-physical systems. Hybrid automata can be considered as generalizations of
finite state automata augmented with a finite set of real-valued variables
whose dynamics in each state is governed by a system of ordinary differential
equations. Moreover, the discrete transitions of hybrid automata are guarded by
constraints over the values of these real-valued variables, and enable
discontinuous jumps in the evolution of these variables. Singular hybrid
automata are a subclass of hybrid automata where dynamics is specified by
state-dependent constant vectors. Henzinger, Kopke, Puri, and Varaiya showed
that for even very restricted subclasses of singular hybrid automata, the
fundamental verification questions, like reachability and schedulability, are
undecidable. In this paper we present \emph{weak singular hybrid automata}
(WSHA), a previously unexplored subclass of singular hybrid automata, and show
the decidability (and the exact complexity) of various verification questions
for this class including reachability (NP-Complete) and LTL model-checking
(PSPACE-Complete). We further show that extending WSHA with a single
unrestricted clock or extending WSHA with unrestricted variable updates lead to
undecidability of reachability problem
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
Sensor Synthesis for POMDPs with Reachability Objectives
Partially observable Markov decision processes (POMDPs) are widely used in
probabilistic planning problems in which an agent interacts with an environment
using noisy and imprecise sensors. We study a setting in which the sensors are
only partially defined and the goal is to synthesize "weakest" additional
sensors, such that in the resulting POMDP, there is a small-memory policy for
the agent that almost-surely (with probability~1) satisfies a reachability
objective. We show that the problem is NP-complete, and present a symbolic
algorithm by encoding the problem into SAT instances. We illustrate trade-offs
between the amount of memory of the policy and the number of additional sensors
on a simple example. We have implemented our approach and consider three
classical POMDP examples from the literature, and show that in all the examples
the number of sensors can be significantly decreased (as compared to the
existing solutions in the literature) without increasing the complexity of the
policies.Comment: arXiv admin note: text overlap with arXiv:1511.0845
PMK : a knowledge processing framework for autonomous robotics perception and manipulation
Autonomous indoor service robots are supposed to accomplish tasks, like serve a cup, which involve manipulation actions. Particularly, for complex manipulation tasks which are subject to geometric constraints, spatial information and a rich semantic knowledge about objects, types, and functionality are required, together with the way in which these objects can be manipulated. In this line, this paper presents an ontological-based reasoning framework called Perception and Manipulation Knowledge (PMK) that includes: (1) the modeling of the environment in a standardized way to provide common vocabularies for information exchange in human-robot or robot-robot collaboration, (2) a sensory module to perceive the objects in the environment and assert the ontological knowledge, (3) an evaluation-based analysis of the situation of the objects in the environment, in order to enhance the planning of manipulation tasks. The paper describes the concepts and the implementation of PMK, and presents an example demonstrating the range of information the framework can provide for autonomous robots.Peer ReviewedPostprint (published version
Reachability in Restricted Chemical Reaction Networks
The popularity of molecular computation has given rise to several models of
abstraction, one of the more recent ones being Chemical Reaction Networks
(CRNs). These are equivalent to other popular computational models, such as
Vector Addition Systems and Petri-Nets, and restricted versions are equivalent
to Population Protocols. This paper continues the work on core reachability
questions related to Chemical Reaction Networks; given two configurations, can
one reach the other according to the system's rules? With no restrictions,
reachability was recently shown to be Ackermann-complete, this resolving a
decades-old problem.
Here, we fully characterize monotone reachability problems based on various
restrictions such as the rule size, the number of rules that may create a
species (k-source) or consume a species (k-consuming), the volume, and whether
the rules have an acyclic production order (feed-forward). We show
PSPACE-completeness of reachability with only bimolecular reactions with
two-source and two-consuming rules. This proves hardness of reachability in
Population Protocols, which was unknown. Further, this shows reachability in
CRNs is PSPACE-complete with size-2 rules, which was previously only known with
size-5 rules. This is achieved using techniques within the motion planning
framework.
We provide many important results for feed-forward CRNs where rules are
single-source or single-consuming. We show that reachability is solvable in
polynomial time if the system does not contain special void or autogenesis
rules. We then fully characterize all systems of this type and show that if you
allow void/autogenesis rules, or have more than one source and one consuming,
the problems become NP-complete. Finally, we show several interesting special
cases of CRNs based on these restrictions or slight relaxations and note future
significant open questions related to this taxonomy.Comment: This research was supported in part by National Science Foundation
Grant CCF-181760
Reachability in Restricted Chemical Reaction Networks
The popularity of molecular computation has given rise to several models of abstraction, one of the more recent ones being Chemical Reaction Networks (CRNs). These are equivalent to other popular computational models, such as Vector Addition Systems and Petri-Nets, and restricted versions are equivalent to Population Protocols. This paper continues the work on core reachability questions related to Chemical Reaction Networks; given two configurations, can one reach the other according to the system\u27s rules? With no restrictions, reachability was recently shown to be Ackermann-complete, this resolving a decades-old problem.Here, we fully characterize monotone reachability problems based on various restrictions such as the rule size, the number of rules that may create a species (k-source) or consume a species (k-consuming), the volume, and whether the rules have an acyclic production order (feed-forward). We show PSPACE-completeness of reachability with only bimolecular reactions with two-source and two-consuming rules. This proves hardness of reachability in Population Protocols, which was unknown. Further, this shows reachability in CRNs is PSPACE-complete with size-2 rules, which was previously only known with size-5 rules. This is achieved using techniques within the motion planning framework.We provide many important results for feed-forward CRNs where rules are single-source or single-consuming. We show that reachability is solvable in polynomial time if the system does not contain special void or autogenesis rules. We then fully characterize all systems of this type and show that if you allow void/autogenesis rules, or have more than one source and one consuming, the problems become NP-complete. Finally, we show several interesting special cases of CRNs based on these restrictions or slight relaxations and note future significant open questions related to this taxonomy
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