scholarly journals Declarative Programming with Temporal Constraints, in the LanguageCG

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Lorina Negreanu
Keyword(s):  
Computational Complexity ◽  
Ambient Intelligence ◽  
Temporal Databases ◽  
Time Dependent ◽  
Knowledge Representation And Reasoning ◽  
Declarative Programming ◽  
Program Execution ◽  
Temporal Constraints ◽  
Straightforward Manner ◽  
Agent Programming

Specifying and interpreting temporal constraints are key elements of knowledge representation and reasoning, with applications in temporal databases, agent programming, and ambient intelligence. We present and formally characterize the languageCG, which tackles this issue. InCG, users are able to develop time-dependent programs, in a flexible and straightforward manner. Such programs can, in turn, be coupled with evolving environments, thus empowering users to control the environment’s evolution.CGrelies on a structure for storing temporal information, together with a dedicated query mechanism. Hence, we explore the computational complexity of our query satisfaction problem. We discuss previous implementation attempts ofCGand introduce a novel prototype which relies on logic programming. Finally, we address the issue of consistency and correctness ofCGprogram execution, using the Event-B modeling approach.

Bayesian Change Detection in the Growing Window Recursive Strategy

2015 ◽  
Vol 775 ◽  
pp. 399-403
Author(s):  
Jakub Dokoupil ◽  
Pavel Václavek
Keyword(s):  
Computational Complexity ◽  
Change Detection ◽  
Change Point ◽  
Distance Measure ◽  
Recursive Algorithm ◽  
Sliding Window ◽  
System Change ◽  
The Other ◽  
Time Dependent ◽  
System Behavior

A novel growing-window recursive algorithm for stochastic system change detection is derived based on the Bayesian inference principle. Model based detectors can be formalized by two concepts in literature: (a) working in a sliding-window strategy because of time-dependent computational complexity, or (b) running in parallel, each one matched to a certain assumption on a change point. This motivates us to investigate a more refined approach which utilizes all relevant data to catch the next change point. The basic idea is to formulate a distance measure between two probabilities, one confirming the change occurrence and the other confirming no change in the system behavior. This study aims to solve the difficulty of sliding time arguments in the compared probabilities as new data are sequentially obtained. The outcome of this analysis is an algorithm that recognizes the time and magnitude of the change occurrence.

scholarly journals Computational complexity of time-dependent density functional theory

2014 ◽  
Vol 16 (8) ◽  
pp. 083035 ◽  
Author(s):  
J D Whitfield ◽  
M-H Yung ◽  
D G Tempel ◽  
S Boixo ◽  
A Aspuru-Guzik
Keyword(s):  
Density Functional Theory ◽  
Computational Complexity ◽  
Density Functional ◽  
Time Dependent ◽  
Functional Theory ◽  
Dependent Density

Resolving conflicts in knowledge for ambient intelligence

2015 ◽  
Vol 30 (5) ◽  
pp. 455-513 ◽  
Author(s):  
Martin Homola ◽  
Theodore Patkos ◽  
Giorgos Flouris ◽  
Ján Šefránek ◽  
Alexander Šimko ◽  
...  
Keyword(s):  
Ambient Intelligence ◽  
Autonomous Agents ◽  
State Of The Art ◽  
Critical Issue ◽  
Knowledge Representation And Reasoning ◽  
Paraconsistent Reasoning ◽  
Context Modelling ◽  
Long Time ◽  
Theoretical Issues ◽  
Similar Complex

AbstractAmbient intelligence (AmI) proposes pervasive information systems composed of autonomous agents embedded within the environment who, in orchestration, complement human activity in an intelligent manner. As such, it is an interesting and challenging application area for many computer science fields and approaches. A critical issue in such application scenarios is that the agents must be able to acquire, exchange, and evaluate knowledge about the environment, its users, and their activities. Knowledge populated between the agents in such systems may be contextually dependent, ambiguous, and incomplete. Conflicts may thus naturally arise, that need to be dealt with by the agents in an autonomous way. In this survey, we relate AmI to the area of knowledge representation and reasoning (KR), where conflict resolution has been studied for a long time. We take a look at a number of KR approaches that may be applied: context modelling, multi-context systems, belief revision, ontology evolution and debugging, argumentation, preferences, and paraconsistent reasoning. Our main goal is to describe the state of the art in these fields, and to draw attention of researchers to important theoretical issues and practical challenges that still need to be resolved, in order to reuse the results from KR in AmI systems or similar complex and demanding applications.

scholarly journals Reports on the 2015 AAAI Spring Symposium Series

AI Magazine ◽  
2015 ◽  
Vol 36 (3) ◽  
pp. 113-119
Author(s):  
Nitin Agarwal ◽  
Sean Andrist ◽  
Dan Bohus ◽  
Fei Fang ◽  
Laurie Fenstermacher ◽  
...  
Keyword(s):  
Ambient Intelligence ◽  
Cognitive Enhancement ◽  
Structured Data ◽  
Knowledge Representation And Reasoning ◽  
Commonsense Reasoning ◽  
Palo Alto ◽  
Cyber Threats ◽  
Behavior Mining ◽  
Symposium Series ◽  
Computational Game Theory

The AAAI 2015 Spring Symposium Series was held Monday through Wednesday, March 23-25, at Stanford University near Palo Alto, California. The titles of the seven symposia were Ambient Intelligence for Health and Cognitive Enhancement, Applied Computational Game Theory, Foundations of Autonomy and Its (Cyber) Threats: From Individuals to Interdependence, Knowledge Representation and Reasoning: Integrating Symbolic and Neural Approaches, Logical Formalizations of Commonsense Reasoning, Socio-Technical Behavior Mining: From Data to Decisions, Structured Data for Humanitarian Technologies: Perfect Fit or Overkill? and Turn-Taking and Coordination in Human-Machine Interaction.The highlights of each symposium are presented in this report.

Time-Dependent Shortest Path Problems with Penalties and Limits on Waiting

2020 ◽  
Author(s):  
Edward He ◽  
Natashia Boland ◽  
George Nemhauser ◽  
Martin Savelsbergh
Keyword(s):  
Computational Complexity ◽  
Polynomial Time ◽  
Shortest Path ◽  
Complexity Analysis ◽  
Fundamental Problem ◽  
Algorithm Design ◽  
Weather Conditions ◽  
Time Dependent ◽  
Shortest Path Problems ◽  
The Right

Waiting at the right location at the right time can be critically important in certain variants of time-dependent shortest path problems. We investigate the computational complexity of time-dependent shortest path problems in which there is either a penalty on waiting or a limit on the total time spent waiting at a given subset of the nodes. We show that some cases are nondeterministic polynomial-time hard, and others can be solved in polynomial time, depending on the choice of the subset of nodes, on whether waiting is penalized or constrained, and on the magnitude of the penalty/waiting limit parameter. Summary of Contributions: This paper addresses simple yet relevant extensions of a fundamental problem in Operations Research: the Shortest Path Problem (SPP). It considers time-dependent variants of SPP, which can account for changing traffic and/or weather conditions. The first variant that is tackled allows for waiting at certain nodes but at a cost. The second variant instead places a limit on the total waiting. Both variants have applications in transportation, e.g., when it is possible to wait at certain locations if the benefits outweigh the costs. The paper investigates these problems using complexity analysis and algorithm design, both tools from the field of computing. Different cases are considered depending on which of the nodes contribute to the waiting cost or waiting limit (all nodes, all nodes except the origin, a subset of nodes…). The computational complexity of all cases is determined, providing complexity proofs for the variants that are NP-Hard and polynomial time algorithms for the variants that are in P.

scholarly journals Time-Dependent QoS Aware Best Service Combination Selection

2015 ◽  
Vol 12 (2) ◽  
pp. 1-25 ◽  
Author(s):  
Ikbel Guidara ◽  
Nawal Guermouche ◽  
Tarak Chaari ◽  
Mohamed Jmaiel ◽  
Said Tazi
Keyword(s):  
Service Oriented Architecture ◽  
Selection Process ◽  
Experimental Studies ◽  
Service Selection ◽  
Time Dependent ◽  
Computational Time ◽  
Structural Constraints ◽  
Temporal Constraints ◽  
Business Applications ◽  
Service Oriented

Service Oriented Architecture allows developing complex business applications from existing services. Given that many services are available with the same functionality and with different Quality of Service (QoS) attributes, one common challenge is to select the best service combination regarding user's requirements. Existing solutions often consider static QoS values for candidate services. Nevertheless, in real world applications, QoS values can change during time. In addition, besides structural constraints, several QoS and temporal constraints can also be specified at the business level. Considering time-dependent QoS values associated with business level constraints makes the selection process a very complex and time consuming decision problem given the large number of service combinations to be compared. To deal with this issue, in this paper, the authors propose a novel service selection approach based on QoS and temporal pruning techniques to reduce the number of candidate services. The proposed approach allows pruning uninteresting services based on a set of local thresholds. These latter are measured using constraint optimization models while dealing with general flow structures including sequential, parallel, choice and loop patterns and different types of QoS and temporal constraints. Experimental studies show the benefits of the proposed approach in particular in terms of computational time.

scholarly journals Complexity Bounds for the Controllability of Temporal Networks with Conditions, Disjunctions, and Uncertainty (Extended Abstract)

2019 ◽  
Author(s):  
Nikhil Bhargava ◽  
Brian C. Williams
Keyword(s):  
Computational Complexity ◽  
Real World ◽  
Temporal Constraints ◽  
Temporal Network ◽  
Temporal Networks ◽  
Temporal Uncertainty ◽  
Complexity Bounds ◽  
Temporal Planning ◽  
Underlying Network

In temporal planning, many different temporal network formalisms are used to model real world situations. Each of these formalisms has different features which affect how easy it is to determine whether the underlying network of temporal constraints is consistent. While many of the simpler models have been well-studied from a computational complexity perspective, the algorithms developed for advanced models which combine features have very loose complexity bounds. In this work, we provide tight completeness bounds for strong, weak, and dynamic controllability checking of temporal networks that have conditions, disjunctions, and temporal uncertainty. Our work exposes some of the subtle differences between these different structures and, remarkably, establishes a guarantee that all of these problems are computable in PSPACE.

Mining Topological Patterns in Spatio-Temporal Databases

2008 ◽  
pp. 130-165
Author(s):  
Wynne Hsu ◽  
Mong Li Lee ◽  
Junmei Wang
Keyword(s):  
Time Window ◽  
Temporal Databases ◽  
Temporal Constraints ◽  
Pattern Growth ◽  
Spatio Temporal

In this chapter, we study the problem of mining topological patterns by imposing temporal constraints into the process of mining collocation patterns. We first introduce a summary structure that summarizes the database with the instances’ count information of a feature in a region within a time window. Next, based on the summary structure, we design an algorithm, called TopologyMiner, to find the interesting topological patterns in a depth-first manner. The algorithm follows the pattern growth methodology. We also investigate an efficient way to incorporate geographical features in TopologyMiner.

scholarly journals Complexity Results and Algorithms for Extension Enforcement in Abstract Argumentation

2017 ◽  
Vol 60 ◽  
pp. 1-40 ◽  
Author(s):  
Johannes P. Wallner ◽  
Andreas Niskanen ◽  
Matti Järvisalo
Keyword(s):  
Computational Complexity ◽  
Formal Model ◽  
Legal Reasoning ◽  
Knowledge Representation And Reasoning ◽  
Polynomial Hierarchy ◽  
Prototype System ◽  
Multi Agent Systems ◽  
Abstract Argumentation ◽  
Complexity Results ◽  
Complete Problems

Argumentation is an active area of modern artificial intelligence (AI) research, with connections to a range of fields, from computational complexity theory and knowledge representation and reasoning to philosophy and social sciences, as well as application-oriented work in domains such as legal reasoning, multi-agent systems, and decision support. Argumentation frameworks (AFs) of abstract argumentation have become the graph-based formal model of choice for many approaches to argumentation in AI, with semantics defining sets of jointly acceptable arguments, i.e., extensions. Understanding the dynamics of AFs has been recently recognized as an important topic in the study of argumentation in AI. In this work, we focus on the so-called extension enforcement problem in abstract argumentation as a recently proposed form of argumentation dynamics. We provide a nearly complete computational complexity map of argument-fixed extension enforcement under various major AF semantics, with results ranging from polynomial-time algorithms to completeness for the second level of the polynomial hierarchy. Complementing the complexity results, we propose algorithms for NP-hard extension enforcement based on constraint optimization under the maximum satisfiability (MaxSAT) paradigm. Going beyond NP, we propose novel MaxSAT-based counterexample-guided abstraction refinement procedures for the second-level complete problems and present empirical results on a prototype system constituting the first approach to extension enforcement in its generality.

Sign in / Sign up

Export Citation Format

Share Document