Contingent Planning Research Articles

Simulated Penetration Testing as Contingent Planning

In penetration testing (pentesting), network administrators attack their own network to identify and fix vulnerabilities. Planning-based simulated pentesting can achieve much higher testing coverage than manual pentesting. A key challenge is for the attack planning to imitate human hackers as faithfully as possible. POMDP models have been proposed to this end, yet they are computationally very hard, and it is unclear how to acquire the models in practice. At the other extreme, classical planning models are scalable and simple to obtain, yet completely ignore the incomplete knowledge characteristic of hacking. We propose contingent planning as a new middle ground, feasible in both computation burden and model acquisition effort while allowing for a representation of incomplete knowledge. We design the model, show how to adapt available solvers, and show how to acquire the model from real network scans in practice. We experiment on real networks and show that our approach scales to practical input sizes.

Landmark-based heuristic online contingent planning

In contingent planning problems, agents have partial information about their state and use sensing actions to learn the value of some variables. When sensing and actuation are separated, plans for such problems can often be viewed as a tree of sensing actions, separated by conformant plans consisting of non-sensing actions that enable the execution of the next sensing action. We propose a heuristic, online method for contingent planning which focuses on identifying the next useful sensing action. We select the next sensing action based on a landmark heuristic, adapted from classical planning. We discuss landmarks for plan trees, providing several alternative definitions and discussing their merits. The key part of our planner is the novel landmarks-based heuristic, together with a projection method that uses classical planning to solve the intermediate conformant planning problems. The resulting heuristic contingent planner solves many more problems than state-of-the-art, translation-based online contingent planners, and in most cases, much faster, up to 3 times faster on simple problems, and 200 times faster on non-simple domains.

When daily planning improves employee performance: The importance of planning type, engagement, and interruptions.

Does planning for a particular workday help employees perform better than on other days they fail to plan? We investigate this question by identifying 2 distinct types of daily work planning to explain why and when planning improves employees' daily performance. The first type is time management planning (TMP)-creating task lists, prioritizing tasks, and determining how and when to perform them. We propose that TMP enhances employees' performance by increasing their work engagement, but that these positive effects are weakened when employees face many interruptions in their day. The second type is contingent planning (CP) in which employees anticipate possible interruptions in their work and plan for them. We propose that CP helps employees stay engaged and perform well despite frequent interruptions. We investigate these hypotheses using a 2-week experience-sampling study. Our findings indicate that TMP's positive effects are conditioned upon the amount of interruptions, but CP has positive effects that are not influenced by the level of interruptions. Through this study, we help inform workers of the different planning methods they can use to increase their daily motivation and performance in dynamic work environments. (PsycINFO Database Record

The Role of Planning as a Fundamental Management Function for Achieving Effectiveness in Business Organizations

The main aim of the paper is to investigate the process of planning as one of the fundamental management functions and tools in more details. Moreover, the paper makes an overview of all advantages of implementing the well planning system for achieving best companies’ results. Planning is a process of setting companies’ goals, making assumptions about the business environment in which those goals should be archived and making a choice about the actions, means and ways in order to achieve the goals. Actually, planning is a system activity that determines when, how and who will perform a separate work activity. In fact, planning is a process that managers apply to identify and choose appropriate goals and directions for action for the organization. On the other hand, the plan is a list of steps and procedures for using time and resources to achieve the specified goals. The fact that planning system has significant advantages for organizations indicate that each business organization should include planning at all levels of management, with the aim of making a quality strategy. Accordingly, there is: operational planning, tactical planning, strategic planning and contingent (multivariate) planning. It’s no matter what king of planning the company is trying to implement, it’s not an easy job. Hence, the effectiveness in making a good planning system as a step towards creating the best corporate strategy is essential for achieving the best business results. In fact, the planning system minimizes uncertainty, facilitates coordination, improves moral principles, increases effectiveness and efficiency, facilitates control.

Evaluation of drought using SPEI drought class transitions and log-linear models for different agro-ecological regions of India

Markov chain and 3-dimensional log-linear models were attempted to model drought class transitions derived from the newly developed drought index the Standardized Precipitation Evapotranspiration Index (SPEI) at a 12 month time scale for six major drought prone areas of India. Log-linear modelling approach has been used to investigate differences relative to drought class transitions using SPEI-12 time series derived form 48 yeas monthly rainfall and temperature data. In this study, the probabilities of drought class transition, the mean residence time, the 1, 2 or 3 months ahead prediction of average transition time between drought classes and the drought severity class have been derived. Seasonality of precipitation has been derived for non-homogeneous Markov chains which could be used to explain the effect of the potential retreat of drought. Quasi-association and Quasi-symmetry log-linear models have been fitted to the drought class transitions derived from SPEI-12 time series. The estimates of odds along with their confidence intervals were obtained to explain the progression of drought and estimation of drought class transition probabilities. For initial months as the drought severity increases the calculated odds shows lower value and the odds decreases for the succeeding months. This indicates that the ratio of expected frequencies of occurrence of transition from drought class to the non-drought class decreases as compared to transition to any drought class when the drought severity of the present class increases. From 3-dimensional log-linear model it is clear that during the last 24 years the drought probability has increased for almost all the six regions. The findings from the present study will immensely help to assess the impact of drought on the gross primary production and to develop future contingent planning in similar regions worldwide.

Farmers' prioritization of climate-smart agriculture (CSA) technologies

Addressing climate change impacts on agriculture is special challenge. There are number of factors that influence the extent to which farmers in a particular location adopt CSA technologies. This study applied a participatory assessment method to assess farmers' preferences and willingness-to-pay for selected CSA practices and technologies in diverse rainfall zones. The study found that farmers' preferences for CSA technologies are marked by some commonalities as well as differences according to their socio-economic characteristics and rainfall zones. The most preferred technologies by local farmers were crop insurance, weather-based crop agro-advisories, rainwater harvesting, site-specific integrated nutrient management, contingent crop planning and laser land levelling. The results also indicate that farmers' preferences and willingness-to-pay are influenced by technologies and their cost of implementation. This study shows the potential for using a participatory CSA prioritization approach to provide information on climate change adaptation planning at local level.

Online belief tracking using regression for contingent planning

In online contingent planning under partial observability an agent decides at each time step on the next action to execute, given its initial knowledge of the world, the actions executed so far, and the observation made. Such agents require some representation of their belief state to determine which actions are valid, or whether the goal has been achieved. Efficient maintenance of a belief state is, given its potential exponential size, a key research challenge in this area. In this paper we develop the theory of regression as a useful tool for belief-state maintenance. We provide a formal description of regression, discussing various alternatives and optimization techniques, and analyze its space and time complexity. In particular, we show that, with some care, the regressed formula will contain variables relevant to the current query only, rather than all variables in the problem description. Consequently, under suitable assumptions, the complexity of regression queries is at most exponential in its contextual width. This parameter is always upper bounded by Bonet and Geffner's width parameter, introduced in their state-of-the-art factored belief tracking (FBT) method. In addition, we show how to obtain a poly-sized circuit representation for the online regression formula even with non-deterministic actions. We provide an empirical comparison of regression with FBT-based belief maintenance, showing the power of regression for online belief tracking. We also suggest caching techniques for regression, and demonstrate their value in reducing runtime in current benchmarks.

Rainfall probability analysis for contingent crop planning in Keonjhar (Odisha)

Rainfall probability analysis for contingent crop planning in Keonjhar (Odisha)

East Asia Industrial Conversion Activity: Outlook at Post-Disaster Crisis Assessments with Technology Integration and Competitive Assimilation Modes

The analytic focus developed in this study has been about providing some evidence on the affirmation of firms' industrial and semi-industrial development stages with production strategies that have formed elements for conversion changes in the East Asian manufacturing processes. To this, there is also the moderate intensity of states' contingency plans prepared to support the formation of consumers' demand for service distribution capacities that have been considered for this comparative review. In fact, the organizational supporting framework of states with regional built-in mechanisms for competition and outsourcing activities relates to contingent planning. However, the diffusion of firms' learning mechanisms evolving toward technological projected outcomes has been implemented with integrative management operations carried out under different modeling programs. Basically, this research has been focused on a comparative discussion about the industrial restructuring stages for the implementation of industrial modeling actions, which have provided a theoretical basic ground that involves the association of environmental concepts referring to Climate Change Adaptability (CAA) for the post-disaster influential implications.

Practical Undoability Checking via Contingent Planning

We consider a general concept of undoability, asking whether a given action can always be undone, no matter which state it is applied to. This generalizes previous concepts of invertibility, and is relevant for search as well as applications. Naïve undoability checking requires to enumerate all states an action is applicable to. Extending and operationalizing prior work in this direction, we introduce a compilation into contingent planning, replacing such enumeration by standard techniques handling large belief states. We furthermore introduce compilations for checking whether one can always get back to an at-least-as-good state, as well as for determining partial undoability, i.e., undoability on a subset of states an action is applicable to. Our experiments on IPC benchmarks and in a cloud management application show that contingent planners are often effective at solving this kind of problem, hence providing a practical means for undoability checking.

Computing Contingent Plans Using Online Replanning

In contingent planning under partial observability with sensing actions, agents actively use sensing to discover meaningful facts about the world. For this class of problems the solution can be represented as a plan tree, branching on various possible observations. Recent successful approaches translate the partially observable contingent problem into a non-deterministic fully observable problem, and then use a planner for non-deterministic planning. While this approach has been successful in many domains, the translation may become very large, encumbering the task of the non-deterministic planner. In this paper we suggest a different approach - using an online contingent solver repeatedly to construct a plan tree. We execute the plan returned by the online solver until the next observation action, and then branch on the possible observed values, and replan for every branch independently. In many cases a plan tree can be exponential in the number of state variables, but still, the tree has a structure that allows us to compactly represent it using a directed graph. We suggest a mechanism for tailoring such a graph that reduces both the computational effort and the storage space. Furthermore, unlike recent state of the art offline planners, our approach is not bounded to a specific class of contingent problems, such as limited problem width, or simple contingent problems. We present a set of experiments, showing our approach to scale better than state of the art offline planners.

Cost-Sensitive Probabilistic Contingent Planning for Web Service Composition

This article presents a cost sensitive probabilistic contingent planning approach for automated semantic web service composition, under the assumptions that the execution of each web service incurs some cost; its alternative outcomes along with their probabilities of occurring are known in advance; and actions do not involve delete effects. The implemented planner, MAPPPA2, produces a contingent plan in the form of a decision tree, by integrating multiple alternative deterministic plans computed by solving a determinized version of the original problem. Both the generation of the alternative deterministic plans, as well as the merging process attempt to maximize the expected utility of the final contingent plan. The article presents evaluation results regarding the approach, based on three web service composition domains.

Protection and utilization of plant biodiversity resources in dry valleys of Southwest China

The dry valleys of Southwest China specifically refer to a type of azonal landscape distributed in the large river basins of the Hengduan Mountain ranges, and are the outcome of the effects of the monsoon climate, ridge-valley geomorphology, and long-term human perturbations. The dry valley landscape is characterized by prominent seasonal aridity, a vertical climate gradient, rich flora of high endemism, and unique vegetation types well adapted to the distinct environment. The environmental change in the last decades is characterized by consistent climate warming and widespread anthropogenic disturbances, especially land use/land cover changes mainly driven by road building, hydro-power construction, mining, and urbanization. These human disturbances intensified vegetation fragmentation, soil erosion, habitat degradation and biological invasion. Environmental degradation has threatened biodiversity resources and ecosystem security while the conservation measures for this unique ecosystem are lacking. We appeal for attention and timely action of biodiversity conservation in the dry valleys of Southwest China by means of establishing natural reserves. Meanwhile, contingent planning for vertical agriculture, degraded vegetation restoration, utilization program for the unique species resource and ecotourism are critical maintaining the sustainability of the dry valley ecosystems. Moreover, the policy and strategy under the goal of ecological civilization construction are also important tools to be applied in promoting ecosystem services and ecological security in the dry valleys of Southwest China. •综述• 476 生 物 多 样 性 Biodiversity Science 第 24 卷

Partially Observable Online Contingent Planning Using Landmark Heuristics

In contingent planning problems, agents have partial information about their state anduse sensing actions to learn the value of some variables.When sensing and actuation are separated, plans for such problems can often be viewed as a tree of sensing actions, separated by conformant plans consisting of non-sensing actions that enable the execution of the next sensing action. This leads us to propose a heuristic, online method for contingent planning which focuses on identifying thenext useful sensing action. The key part of our planner is a novel landmarks-based heuristic for selecting the next sensing action, together with a projection method that uses classical planning to solve the intermediate conformant planning problems.This allows our planner to operate without an explicit model of belief space or the use of existing translation techniques,both of which can require exponential space. The resulting Heuristic Contingent Planner (HCP) solves many more problems than state-of-the-art, translation-based online contingent planners, and in most cases much faster.

Fault Tolerant Planning: Complexity and Compilation

In the context of modeling and reasoning about agent actions, contingent and classical planning can often be respectively seen as adopting ``extreme pessimism'' and ``extreme optimism'' about the action outcomes. For many everyday scenarios of human reasoning (and thus for many types of autonomous systems), both these approaches are just too extreme. Following Jensen, Veloso, and Bryant (2004), we examine a planning model that interpolates between classical and contingent planning via tolerance to arbitrary k faults occurring during plan execution. We show that an important fragment of this fault tolerant planning (FT-planning) exhibits both an appealing solution structure, as well as appealing worst-case time-complexity properties. We also show that such FT-planning tasks can be efficiently compiled into classical planning as long as the number of possible faults per operator is bounded by a constant, and we show that this compilation can be attractive in practice.

Phase Transitions of Contingent Planning Problem

This paper explores the phase transitions of the contingent planning problems. We present CONTINGENT PLAN-EXISTENCE algorithm and CONTINGENT PLAN-NONEXISTENCE algorithm for quickly proving that the contingent planning instances have solutions and have no solutions, respectively. By analyzing the two algorithms, the phase transition area of the contingent planning problems is obtained. If the number of the actions is not greater thanθub, the CONTINGENT PLAN-NONEXISTENCE algorithm can prove that nearly all the contingent planning instances have no solution. If the number of the actions is not lower thanθlb, the CONTINGENT PLAN-EXISTENCE algorithm can prove that nearly all the contingent planning instances have solutions. The results of the experiments show that there exist phase transitions from a region where almost all the contingent planning instances have no solution to a region where almost all the contingent planning instances have solutions.

Conjunctive Representations in Contingent Planning: Prime Implicates Versus Minimal CNF Formula

This paper compares in depth the effectiveness of two conjunctive belief state representations in contingent planning: prime implicates and minimal CNF, a compact form of CNF formulae, which were initially proposed in conformant planning research (To et al. 2010a; 2010b). Similar to the development of the contingent planner CNFct for minimal CNF (To et al. 2011b), the present paper extends the progression function for the prime implicate representation in (To et al. 2010b) for computing successor belief states in the presence of incomplete information to handle non-deterministic and sensing actions required in contingent planning. The idea was instantiated in a new contingent planner, called PIct, using the same AND/OR search algorithm and heuristic function as those for CNFct. The experiments show that, like CNFct, PIct performs very well in a wide range of benchmarks. The study investigates the advantages and disadvantages of the two planners and identifies the properties of each representation method that affect the performance.

On the Effectiveness of Belief State Representation in Contingent Planning

This work proposes new approaches to contingent planning using alternative belief state representations extended from those in conformant planning and a new AND/OR forward search algorithm, called PrAO, for contingent solutions. Each representation was implemented in a new contingent planner. The important role of belief state representation has been confirmed by the fact that our planners all outperform other stateof- the-art planners on most benchmarks and the comparison of their performances varies across all the benchmarks even using the same search algorithm PrAO and same unsophisticated heuristic scheme. The work identifies the properties of each representation method that affect the performance.

Contingent Planning as AND/OR Forward Search with Disjunctive Representation

This paper introduces a highly competitive contingent planner, that uses the novel idea of encoding belief states as disjunctive normal form formulae (To et al. 2009), for the search for solutions in the belief state space. In (To et al. 2009), a complete transition function for computing successor belief states in the presence of incomplete information has been defined. This work extends the function to handle non-deterministic and sensing actions in the AND/OR forward search paradigm for contingent planning solutions. The function allows one, under reasonable assumptions, to compute successor belief states efficiently, i.e., in polynomial time. The paper also presents a novel variant of an AND/OR search algorithm, called PrAO (Pruning AND/OR search), which allows the planner to significantly prune the search space; furthermore, by the time a solution is found, the remaining search graph is also the solution tree for the contingent planing problem. The strength of these techniques is confirmed by the empirical results obtained from a large set of benchmarks available in the literature.

조건부 계획수립을 위한 효과적인 그래프 기반의 휴리스틱

In order to derive domain-independent heuristics from the specification of a planning problem, it is required to relax the given problem and then solve the relaxed one. In this paper, we present a new planning graph, Merged Planning Graph(MPG), and GD heuristics for solving contingent planning problems with both uncertainty about the initial state and non-deterministic action effects. The merged planning graph is an extended one to be applied to the contingent planning problems from the relaxed planning graph, which is a common means to get effective heuristics for solving the classical planning problems. In order to get heuristics for solving the contingent planning problems with sensing actions and non-deterministic actions, the new graph utilizes additionally the effect-merge relaxations of these actions as well as the traditional delete relaxations. Proceeding parallel to the forward expansion of the merged planning graph, the computation of GD heuristic excludes the unnecessary redundant cost from estimating the minimal reachability cost to achieve the overall set of goals by analyzing interdependencies among goals or subgoals. Therefore, GD heuristics have the advantage that they usually require less computation time than the overlap heuristics, but are more informative than the max and the additive heuristics. In this paper, we explain the experimental analysis to show the accuracy and the search efficiency of the GD heuristics.