394,509 research outputs found
A Logic Programming Approach to Knowledge-State Planning: Semantics and Complexity
We propose a new declarative planning language, called K, which is based on
principles and methods of logic programming. In this language, transitions
between states of knowledge can be described, rather than transitions between
completely described states of the world, which makes the language well-suited
for planning under incomplete knowledge. Furthermore, it enables the use of
default principles in the planning process by supporting negation as failure.
Nonetheless, K also supports the representation of transitions between states
of the world (i.e., states of complete knowledge) as a special case, which
shows that the language is very flexible. As we demonstrate on particular
examples, the use of knowledge states may allow for a natural and compact
problem representation. We then provide a thorough analysis of the
computational complexity of K, and consider different planning problems,
including standard planning and secure planning (also known as conformant
planning) problems. We show that these problems have different complexities
under various restrictions, ranging from NP to NEXPTIME in the propositional
case. Our results form the theoretical basis for the DLV^K system, which
implements the language K on top of the DLV logic programming system.Comment: 48 pages, appeared as a Technical Report at KBS of the Vienna
University of Technology, see http://www.kr.tuwien.ac.at/research/reports
Contingent task and motion planning under uncertainty for human–robot interactions
Manipulation planning under incomplete information is a highly challenging task for mobile manipulators. Uncertainty can be resolved by robot perception modules or using human knowledge in the execution process. Human operators can also collaborate with robots for the execution of some difficult actions or as helpers in sharing the task knowledge. In this scope, a contingent-based task and motion planning is proposed taking into account robot uncertainty and human–robot interactions, resulting a tree-shaped set of geometrically feasible plans. Different sorts of geometric reasoning processes are embedded inside the planner to cope with task constraints like detecting occluding objects when a robot needs to grasp an object. The proposal has been evaluated with different challenging scenarios in simulation and a real environment.Postprint (published version
Knowledge-Based Programs as Plans: Succinctness and the Complexity of Plan Existence
Knowledge-based programs (KBPs) are high-level protocols describing the
course of action an agent should perform as a function of its knowledge. The
use of KBPs for expressing action policies in AI planning has been surprisingly
overlooked. Given that to each KBP corresponds an equivalent plan and vice
versa, KBPs are typically more succinct than standard plans, but imply more
on-line computation time. Here we make this argument formal, and prove that
there exists an exponential succinctness gap between knowledge-based programs
and standard plans. Then we address the complexity of plan existence. Some
results trivially follow from results already known from the literature on
planning under incomplete knowledge, but many were unknown so far.Comment: 10 pages, Contributed talk at TARK 2013 (arXiv:1310.6382)
http://www.tark.or
Les POMDP font de meilleurs hackers: Tenir compte de l'incertitude dans les tests de penetration
Penetration Testing is a methodology for assessing network security, by
generating and executing possible hacking attacks. Doing so automatically
allows for regular and systematic testing. A key question is how to generate
the attacks. This is naturally formulated as planning under uncertainty, i.e.,
under incomplete knowledge about the network configuration. Previous work uses
classical planning, and requires costly pre-processes reducing this uncertainty
by extensive application of scanning methods. By contrast, we herein model the
attack planning problem in terms of partially observable Markov decision
processes (POMDP). This allows to reason about the knowledge available, and to
intelligently employ scanning actions as part of the attack. As one would
expect, this accurate solution does not scale. We devise a method that relies
on POMDPs to find good attacks on individual machines, which are then composed
into an attack on the network as a whole. This decomposition exploits network
structure to the extent possible, making targeted approximations (only) where
needed. Evaluating this method on a suitably adapted industrial test suite, we
demonstrate its effectiveness in both runtime and solution quality.Comment: JFPDA 2012 (7\`emes Journ\'ees Francophones Planification,
D\'ecision, et Apprentissage pour la conduite de syst\`emes), Nancy, Franc
Human Robot Collaborative Assembly Planning: An Answer Set Programming Approach
For planning an assembly of a product from a given set of parts, robots
necessitate certain cognitive skills: high-level planning is needed to decide
the order of actuation actions, while geometric reasoning is needed to check
the feasibility of these actions. For collaborative assembly tasks with humans,
robots require further cognitive capabilities, such as commonsense reasoning,
sensing, and communication skills, not only to cope with the uncertainty caused
by incomplete knowledge about the humans' behaviors but also to ensure safer
collaborations. We propose a novel method for collaborative assembly planning
under uncertainty, that utilizes hybrid conditional planning extended with
commonsense reasoning and a rich set of communication actions for collaborative
tasks. Our method is based on answer set programming. We show the applicability
of our approach in a real-world assembly domain, where a bi-manual Baxter robot
collaborates with a human teammate to assemble furniture. This manuscript is
under consideration for acceptance in TPLP.Comment: 36th International Conference on Logic Programming (ICLP 2020),
University Of Calabria, Rende (CS), Italy, September 2020, 15 page
Robot task planning and explanation in open and uncertain worlds
A long-standing goal of AI is to enable robots to plan in the face of uncertain and incomplete information, and to handle task failure intelligently. This paper shows how to achieve this. There are two central ideas. The first idea is to organize the robot's knowledge into three layers: instance knowledge at the bottom, commonsense knowledge above that, and diagnostic knowledge on top. Knowledge in a layer above can be used to modify knowledge in the layer(s) below. The second idea is that the robot should represent not just how its actions change the world, but also what it knows or believes. There are two types of knowledge effects the robot's actions can have: epistemic effects (I believe X because I saw it) and assumptions (I'll assume X to be true). By combining the knowledge layers with the models of knowledge effects, we can simultaneously solve several problems in robotics: (i) task planning and execution under uncertainty; (ii) task planning and execution in open worlds; (iii) explaining task failure; (iv) verifying those explanations. The paper describes how the ideas are implemented in a three-layer architecture on a mobile robot platform. The robot implementation was evaluated in five different experiments on object search, mapping, and room categorization
Using Knowledge Awareness to improve Safety of Autonomous Driving
We present a method, which incorporates knowledge awareness into the symbolic
computation of discrete controllers for reactive cyber physical systems, to
improve decision making about the unknown operating environment under
uncertain/incomplete inputs. Assuming an abstract model of the system and the
environment, we translate the knowledge awareness of the operating context into
linear temporal logic formulas and incorporate them into the system
specifications to synthesize a controller. The knowledge base is built upon an
ontology model of the environment objects and behavioural rules, which includes
also symbolic models of partial input features. The resulting symbolic
controller support smoother, early reactions, which improves the security of
the system over existing approaches based on incremental symbolic perception. A
motion planning case study for an autonomous vehicle has been implemented to
validate the approach, and presented results show significant improvements with
respect to safety of state-of-the-art symbolic controllers for reactive
systems
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