A rewriting framework and logic for activities subject to regulations

2015 ◽  
Vol 27 (3) ◽  
pp. 332-375 ◽  
Author(s):  
MAX KANOVICH ◽  
TAJANA BAN KIRIGIN ◽  
VIVEK NIGAM ◽  
ANDRE SCEDROV ◽  
CAROLYN TALCOTT ◽  
...  
Keyword(s):  
Human Error ◽  
Linear Logic ◽  
Formal Semantics ◽  
Time Constraints ◽  
Negative Consequences ◽  
Initial State ◽  
Goal State ◽  
Model Based ◽  
Compliance Problem ◽  
Planning Problems

Activities such as clinical investigations (CIs) or financial processes are subject to regulations to ensure quality of results and avoid negative consequences. Regulations may be imposed by multiple governmental agencies as well as by institutional policies and protocols. Due to the complexity of both regulations and activities, there is great potential for violation due to human error, misunderstanding, or even intent. Executable formal models of regulations, protocols and activities can form the foundation for automated assistants to aid planning, monitoring and compliance checking. We propose a model based on multiset rewriting where time is discrete and is specified by timestamps attached to facts. Actions, as well as initial, goal and critical states may be constrained by means of relative time constraints. Moreover, actions may have non-deterministic effects, i.e. they may have different outcomes whenever applied. We present a formal semantics of our model based on focused proofs of linear logic with definitions. We also determine the computational complexity of various planning problems. Plan compliance problem, for example, is the problem of finding a plan that leads from an initial state to a desired goal state without reaching any undesired critical state. We consider all actions to be balanced, i.e. their pre- and post-conditions have the same number of facts. Under this assumption on actions, we show that the plan compliance problem is PSPACE-complete when all actions have only deterministic effects and is EXPTIME-complete when actions may have non-deterministic effects. Finally, we show that the restrictions on the form of actions and time constraints taken in the specification of our model are necessary for decidability of the planning problems.

Generation of Probabilistically Optimal Paths for Nonholonomic Needle Insertion

2016 ◽  
Author(s):  
Jianping Lin ◽  
Wooram Park
Keyword(s):  
Cost Function ◽  
Optimal Path ◽  
Target Position ◽  
Nonholonomic Constraints ◽  
Needle Insertion ◽  
Initial State ◽  
Goal State ◽  
Medical Needles ◽  
The Cost ◽  
Planning Problems

Rapidly-exploring Random Tree (RRT) is a sampling-based algorithm which is designed for path planning problems. It is efficient to handle high-dimensional configuration space (C-space) and nonholonomic constraints. Under the nonholonomic constraints, the RRT can generate paths between an initial state and a goal state while avoiding obstacles. Since this framework assumes that a system is deterministic, more improvement should be added when the method is applied to a system with uncertainty. In robotic systems with motion uncertainty, probability for successful targeting and obstacle avoidance are more suitable measurement than the deterministic distance between the robot system and the target position. In this paper, the probabilistic targeting error is defined as a root-mean-square (RMS) distance between the system to the desired target. The proximity of the obstacle to the system is also defined as an averaged distance of obstacles to the robotic system. Then, we consider a cost function that is a sum of the targeting error and the obstacle proximity. By numerically minimizing the cost, we can obtain the optimal path. In this paper, a method for efficient evaluation and minimization of this cost function is proposed and the proposed method is applied to nonholonomic flexible medical needles for performance tests.

scholarly journals Effective grounding for hybrid planning problems represented in PDDL+

2021 ◽  
Vol 36 ◽  
Author(s):  
Enrico Scala ◽  
Mauro Vallati
Keyword(s):  
Real World ◽  
Automated Planning ◽  
Search Space ◽  
Language Models ◽  
State Variables ◽  
Initial State ◽  
Discrete State ◽  
Goal State ◽  
Search Spaces ◽  
Planning Problems

Abstract Automated planning is the field of Artificial Intelligence (AI) that focuses on identifying sequences of actions allowing to reach a goal state from a given initial state. The need of using such techniques in real-world applications has brought popular languages for expressing automated planning problems to provide direct support for continuous and discrete state variables, along with changes that can be either instantaneous or durative. PDDL+ (Planning Domain Definition Language +) models support the encoding of such representations, but the resulting planning problems are notoriously difficult for AI planners to cope with due to non-linear dependencies arising from the variables and infinite search spaces. This difficulty is exacerbated by the potentially huge fully ground representations used by modern planners in order to effectively explore the search space, which can make some problems impossible to tackle. This paper investigates two grounding techniques for PDDL+ problems, both aimed at reducing the size of the full ground representation by reasoning over the lifted, more abstract problem structure. The first method extends the simple mechanism of invariant analysis to limit the groundings of operators upfront. The second method proposes to tackle the grounding process through a PDDL+ to classical planning abstraction; this allows us to leverage the amount of research done in the classical planning area. Our empirical analysis studies the effect of these novel approaches over both real-world hybrid applications and synthetic PDDL+ problems took from standard benchmarks of the planning community; our results reveal that not only the techniques improve the running time of previous grounding mechanisms but also let the planner extend the reach to problems that were not solvable before.

scholarly journals CMIP6 model-based analog forecasting for the seasonal prediction of sea surface temperature in the offshore area of China

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Weiying Peng ◽  
Quanliang Chen ◽  
Shijie Zhou ◽  
Ping Huang
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Climate Models ◽  
Global Climate Models ◽  
Sea Surface ◽  
Seasonal Forecasts ◽  
Initial State ◽  
Initial Field ◽  
Offshore Area ◽  
Model Based

AbstractSeasonal forecasts at lead times of 1–12 months for sea surface temperature (SST) anomalies (SSTAs) in the offshore area of China are a considerable challenge for climate prediction in China. Previous research suggests that a model-based analog forecasting (MAF) method based on the simulations of coupled global climate models provide skillful climate forecasts of tropical Indo-Pacific SSTAs. This MAF method selects the model-simulated cases close to the observed initial state as a model-analog ensemble, and then uses the subsequent evolution of the SSTA to generate the forecasts. In this study, the MAF method is applied to the offshore area of China (0°–45°N, 105°–135°E) based on the simulations of 23 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) for the period 1981–2010. By optimizing the key factors in the MAF method, we suggest that the optimal initial field for the analog criteria should be concentrated in the western North Pacific. The multi-model ensemble of the optimized MAF prediction using these 23 CMIP6 models shows anomaly correlation coefficients exceeding 0.6 at the 3-month lead time, which is much improved relative to previous SST-initialized hindcasts and appears practical for operational forecasting.

scholarly journals Contextualizing Sustainable Development Metric Standards: Imagining New Entrepreneurial Possibilities

2020 ◽  
Vol 12 (22) ◽  
pp. 9661
Author(s):  
William P. Fisher
Keyword(s):  
Sustainable Development ◽  
Boundary Objects ◽  
Negative Consequences ◽  
Model Based ◽  
Ecology Of Mind ◽  
Logical Structures ◽  
Sustainability Problems ◽  
Global And Local ◽  
Logical Types

Imagination is more important than knowledge, but if intellect does not provide the needed logical structures, capacities for envisioning new possibilities are overly constrained. The sustainability problems we face today cannot be solved with the same kind of thinking that created them, but clarity on what counts as a new kind of thinking is sorely lacking. This article proposes methodical, model-based ways of heeding Bateson’s warning about the negative consequences for the ecology of mind that follow from ignoring the contexts of relationships. Informed by S. L. Star’s sense of boundary objects, a sequence of increasingly complex logical types distinguishes and interconnects qualitatively different kinds of thinking in ways that liberate imaginative new possibilities for life. The economy of thought instantiated at each level of complexity is only as meaningful, useful, beautiful, ethical, and efficient as the standards informing local adaptive improvisations. Standards mediating the general and specific, global and local, universally transcendent and embodied particulars enable meaningful negotiations, agreements, and communications. Attending to the differences between levels of discourse sets up new possibilities for creative and imaginative entrepreneurial approaches to viable, feasible, and desirable goals for measuring and managing sustainable development.

scholarly journals Unconventionals in Europe: Best Practice vs. Worst Case - The Conflict Between Facts and Public Perception

2016 ◽  
Vol 23 (3) ◽  
pp. 377-386 ◽  
Author(s):  
Peter Burri
Keyword(s):  
Natural Gas ◽  
Public Perception ◽  
Human Error ◽  
Fossil Fuels ◽  
Fossil Fuel ◽  
Hydrocarbon Exploration ◽  
Negative Consequences ◽  
Rapid Improvement ◽  
Worst Case ◽  
The World

Abstract In spite of great progress in energy efficiency and in the development of renewable energy the world is likely to need significant amounts of fossil fuel throughout this century and beyond (the share of fossil fuels in the world mix has remained at about 86% of primary energy from 1990 to today). Gas, being the by far cleanest fossil fuel is the ideal bridging fuel to a world with predominantly renewable supplies. Thanks to the recent perfection of unconventional technologies there is no shortage of gas for this bridging function for at least the next 100-200 years. EASAC and several other European Institutions, notably the German Academy of Technical Sciences (acatech) have in the last few years carried out expert studies to assess the alleged environmental risks of unconventional hydrocarbon exploration and production. All these studies have, in agreement with other competent studies worldwide, come to the conclusion that there exists no scientific reason for a ban on hydraulic fracturing. With good practices, clear standards and adequate control the method causes no enhanced risks to the environment or the health of humans. Special attention has to be paid to the surface handling of drilling and fracking fluids. In Europe alone many thousand frac jobs have been carried out by the industry in the last 60 years without any severe accidents. The mishaps in North America have largely been the cause of unprofessional operations and human error. Especially in places with high air pollution, like many megacities of Asia, natural gas has to be seen as a unique chance to achieve a rapid improvement of the air quality and a significant reduction of CO2 emissions. This is also true for Europe where especially the use of domestic natural gas brings important benefits to the environment. The alternative to gas is in many regions of the world an increased consumption of coal, with all negative consequences.

scholarly journals Minimum Mode Saddle Point Searches Using Gaussian Process Regression with Inverse-Distance Covariance Function

2019 ◽  
Author(s):  
Olli-Pekka Koistinen ◽  
Vilhjálmur Ásgeirsson ◽  
Aki Vehtari ◽  
Hannes Jónsson
Keyword(s):  
Saddle Point ◽  
Gaussian Process ◽  
Process Model ◽  
Energy Surface ◽  
Saddle Points ◽  
Gaussian Process Regression ◽  
Dissociative Adsorption ◽  
Initial Guess ◽  
Initial State ◽  
Model Based

The minimum mode following method can be used to find saddle points on an energy surface by following a direction guided by the lowest curvature mode. Such calculations are often started close to a minimum on the energy surface to find out which transitions can occur from an initial state of the system, but it is also common to start from the vicinity of a first order saddle point making use of an initial guess based on intuition or more approximate calculations. In systems where accurate evaluations of the energy and its gradient are computationally intensive, it is important to exploit the information of the previous evaluations to enhance the performance. Here, we show that the number of evaluations required for convergence to the saddle point can be significantly reduced by making use of an approximate energy surface obtained by a Gaussian process model based on inverse inter-atomic distances, evaluating accurate energy and gradient at the saddle point of the approximate surface and then correcting the model based on the new information. The performance of the method is tested with start points chosen randomly in the vicinity of saddle points for dissociative adsorption of an H2 molecule on the Cu(110) Surface and three gas phase chemical reactions.<br>

scholarly journals Emotion Learning: Solving a Shortest Path Problem in an Arbitrary Deterministic Environment in Linear Time with an Emotional Agent

2008 ◽  
Vol 18 (3) ◽  
pp. 409-421 ◽  
Author(s):  
Silvana Petruseva
Keyword(s):  
Shortest Path ◽  
Emotional State ◽  
Linear Time ◽  
Decisive Role ◽  
Time Algorithm ◽  
Shortest Path Problem ◽  
Adaptive Array ◽  
Agent Architecture ◽  
Initial State ◽  
Goal State

Emotion Learning: Solving a Shortest Path Problem in an Arbitrary Deterministic Environment in Linear Time with an Emotional AgentThe paper presents an algorithm which solves the shortest path problem in an arbitrary deterministic environment withnstates with an emotional agent in linear time. The algorithm originates from an algorithm which in exponential time solves the same problem, and the agent architecture used for solving the problem is an NN-CAA architecture (neural network crossbar adaptive array). By implementing emotion learning, the linear time algorithm is obtained and the agent architecture is modified. The complexity of the algorithm without operations for initiation in general does not depend on the number of statesn, but only on the length of the shortest path. Depending on the position of the goal state, the complexity can be at mostO (n).It can be concluded that the choice of the function which evaluates the emotional state of the agent plays a decisive role in solving the problem efficiently. That function should give as detailed information as possible about the consequences of the agent's actions, starting even from the initial state. In this way the function implements properties of human emotions.

A COMPROMISE PROGRAMMING APPROACH TO MULTIOBJECTIVE MARKOV DECISION PROCESSES

2013 ◽  
Vol 12 (05) ◽  
pp. 1021-1053 ◽  
Author(s):  
WLODZIMIERZ OGRYCZAK ◽  
PATRICE PERNY ◽  
PAUL WENG
Keyword(s):  
Inventory Management ◽  
Direct Determination ◽  
Weighted Average ◽  
Programming Approach ◽  
Autonomous Agent ◽  
Initial State ◽  
Markov Decision ◽  
Bellman Principle ◽  
Planning Problems

A Markov decision process (MDP) is a general model for solving planning problems under uncertainty. It has been extended to multiobjective MDP to address multicriteria or multiagent problems in which the value of a decision must be evaluated according to several viewpoints, sometimes conflicting. Although most of the studies concentrate on the determination of the set of Pareto-optimal policies, we focus here on a more specialized problem that concerns the direct determination of policies achieving well-balanced tradeoffs. To this end, we introduce a reference point method based on the optimization of a weighted ordered weighted average (WOWA) of individual disachievements. We show that the resulting notion of optimal policy does not satisfy the Bellman principle and depends on the initial state. To overcome these difficulties, we propose a solution method based on a linear programming (LP) reformulation of the problem. Finally, we illustrate the feasibility of the proposed method on two types of planning problems under uncertainty arising in navigation of an autonomous agent and in inventory management.

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