Contingent Planning Research Articles

Domain independent heuristics for online stochastic contingent planning

AbstractPartially observable Markov decision processes (POMDP) are a useful model for decision-making under partial observability and stochastic actions. Partially Observable Monte-Carlo Planning (POMCP) is an online algorithm for deciding on the next action to perform, using a Monte-Carlo tree search approach, based on the UCT algorithm for fully observable Markov-decision processes. POMCP develops an action-observation tree, and at the leaves, uses a rollout policy to provide a value estimate for the leaf. As such, POMCP is highly dependent on the rollout policy to compute good estimates, and hence identify good actions. Thus, many practitioners who use POMCP are required to create strong, domain-specific heuristics. In this paper, we model POMDPs as stochastic contingent planning problems. This allows us to leverage domain-independent heuristics that were developed in the planning community. We suggest two heuristics, the first is based on the well-known $$h_{add}$$ h add heuristic from classical planning, and the second is computed in belief space, taking the value of information into account.

Contingency Planning of Visual Contamination for Wheeled Mobile Robots with Chameleon-Inspired Visual System

To enable mobile robots to effectively deal with the emergency of visual contamination, contingency planning based on case-based reasoning (CBR) was performed in this paper. First, for a wheeled mobile robot (WMR) equipped with a chameleon-inspired visual system, a target search model in chameleon-inspired binocular negative correlation movement (CIBNCM) mode was established. Second, a CBR-based contingency planning model of visual contamination for WMRs was established, where the reasoning process using CBR for visual contamination was analyzed in detail. Third, through the analysis of environment perception when visual contamination occurs, a perception model in chameleon-inspired visual contamination for WMRs was built. Finally, to validate the proposed approach, a contingency planning experiment scheme for visual contamination was designed based on the robot’s general planning of target tracking, and the experimental result is discussed. The proposed CBR-based contingency planning approach for visual contamination can reason out effective solutions corresponding to the contamination situations. The rationality of the approach was verified by experiments with satisfactory results. Moreover, compared with the contingency planning method based on rule-based reasoning, the accuracy of target retracking after the robot visual system is contaminated is significantly higher for the CBR-based contingent planning method used in this paper.

Talent management in hospitality during the COVID-19 pandemic in Macao: a contingency approach

PurposeThis study aims to examine talent management (TM) in the hospitality industry in Macao during COVID-19. It deploys a contingency theory perspective (Luthans and Stewart, 1977) to illuminate the heightened uncertainties and challenges talent managers faced during the pandemic and the urgent adaptations to TM practices they embraced in response.Design/methodology/approachAdopting a phenomenological approach, this study analyzed data collected through semi-structured interviews conducted with a representative sample of 20 hotel managers in Macao.FindingsManagers reported four major categories of COVID-19-induced challenges and a corresponding set of contingent TM practices. The four contingent TM practices were found to contribute to the shaping of the next new normal in TM in hospitality and included the following: Contingent TM planning; contingent TM deployment and replacement; talent training and development under contingent arrangements; and changed “talent” attitude and practices in recruitment and retention.Research limitations/implicationsThe findings are limited to the geographical and industry context of the study. This study should be refined with larger samples.Practical implicationsThis study provides a useful framework for guiding professionals on how to manage talent during turmoil periods. It also contributes toward understanding the shifting meaning of talent and TM in hospitality.Originality/valueThis study demonstrates the applicability of contingency theory in managing hospitality talent during turbulent times, which extends TM knowledge and enriches the contingency theory. The findings also facilitate our understanding on how contingent TM practices create processes that lead in setting the new normal.

Advances in application of sub-seasonal weather forecast in Indian agriculture

Under the climatic variability and climate change, skilful weather forecast in different spatial and temporal scale encourages the farmers to organize and activate their own resources in the best possible way to increase the crop production. Though medium range weather forecast is used extensively in operational agromet advisory services, sub-seasonal forecast provides additional decision-relevant information to support the timing of crop planting, irrigation scheduling, and harvesting, particularly in water-stressed regions. In view of that, dynamical and statistical and sub-seasonal seasonal forecast is generated and delivered to the farmers as climate information services in number of countries in the world. Under the Gramin Krishi Mausam Sewa (GKMS) Project, Agricultural Meteorology Division, India Meteorological Department (IMD) in collaboration with Indian Institute of Tropical Meteorology (IITM), Pune, All India Coordinated Research Project on Agrometeorology (AICRPAM), CRIDA, Indian Council of Agricultural Research, Hyderabad prepared Agromet Advisory fortnightly taking into consideration realized rainfall during previous fortnight and extended range rainfall forecast for next fortnight and crop information i.e., state and stage of the crops. In the present article agricultural applications of sub-seasonal forecasts on agricultural management in India has been explored. It has been showed how the extended range weather forecast i.e., sub-seasonal forecast has been developed and translated into agromet advisories for the farming communities to increase crop production in India and whether the present state of accuracy could be used for generating advisory under contingent crop planning conditions and other advisories by citing different case studies and ultimately helping the farming communities to improve their economic conditions. It has been demonstrated here that sub-seasonal forecasts are increasingly being used across agriculture in the country. The sub-seasonal forecasting time scale is therefore a new concept for many users. Because of the additional value of sub-seasonal forecasts for decision-making, it is increasingly gaining interest among users. Present case studies clearly suggest the forecast at sub-seasonal time scale is need of the hour.

Unavoidable deadends in deterministic partially observable contingent planning

Unavoidable deadends in deterministic partially observable contingent planning

Assessment of Long Term Spatio-temporal Climatic Changes Over the Central India: A GIS Approach

The climatic data of 107 meteorological stations of Central India were collected and maps of various climatic parameters were generated by using ArcGIS 10.5 software. Potential evapotranspiration (PET), rainfall, water deficit and moisture index trends were analyzed for 30 years (1988-2018). PET was computed by using Modified Penman-Monteith method as recommended by FAO. It concludes that though the amount of mean annual rainfall is moreover same but the variation in duration and distribution exists. The analysis shows that number of rainy days has decreased in this long period, which led to increase the PET. High water deficit resulted in shifting of bio-climate towards arid condition. The results indicates that Maximum mean annual rainfall was observed in Mahabaleswar station of Maharashtra (6098 mm) and minimum mean annual rainfall also reported in Malegaon (545 mm) of Maharashtra, in Central India. It was observed that western districts of Madhya Pradesh (Barwani, Alirajpur) are shifting significantly towards drier side during the period of 30 years. Thus, information of long term spatial climatic changes can be effectively used for contingent crop planning, irrigation scheduling and diversification of crops.

When Political Rights Do Not Translate into Economic Power: The Rise and Fall of the Slovenian Minority’s Economy in Italy (1954–2020)

AbstractLack of economic data based on ethnicity makes the study of minority economies in linguistically or ethnically mixed regions problematic. Yet, there is much to learn from these hard cases of economic development in terms of the policies that guarantee ethnic or linguistic survival and reproduction. The present article investigates the long-term economic development of the Slovenian minority in Italy between 1954 and 2020. It does so by applying the theoretical framework by new institutional economists Daron Acemoglu, Simon Johnson, and James Robinson (2005), which looks at the dynamic role of political and economic institutions for the generation of economic growth. The parable of this community’s economy shows that political power and economic success can be flimsy and ephemeral. Strategic, long-term economic planning and, especially, contingent planning against foreseeable risks, both of which require the systematic collection of accurate data and the coordination of representative organizations, are key to successful reproduction and sustained growth.

Width and Complexity of Belief Tracking in Non-Deterministic Conformant and Contingent Planning

It has been shown recently that the complexity of belief tracking in deterministic conformant and contingent planning is exponential in a width parameter that is often bounded and small. In this work, we introduce a new width notion that applies to non-deterministic conformant and contingent problems as well. We also develop a belief tracking algorithm for non-deterministic problems that is exponential in the problem width, analyze the width of non-deterministic benchmarks, compare the new notion to the previous one over deterministic problems, and present experimental results.

A Multi-Path Compilation Approach to Contingent Planning

We describe a new sound and complete method forcompiling contingent planning problems with sensingactions into classical planning. Our method encodesconditional plans within a linear, classicalplan. This allows our planner, MPSR, to reasonabout multiple future outcomes of sensing actions,and makes it less susceptible to dead-ends. MPRS,however, generates very large classical planningproblems. To overcome this, we use an incompletevariant of the method, based on state sampling,within an online replanner. On most currentdomains, MPSR finds plans faster, although itsplans are often longer. But on a new challengingvariant of Wumpus with dead-ends, it finds smallerplans, faster, and scales much better.

A general multi-agent epistemic planner based on higher-order belief change

In recent years, multi-agent epistemic planning has received attention from both dynamic logic and planning communities. Existing implementations of multi-agent epistemic planning are based on compilation into classical planning and suffer from various limitations, such as generating only linear plans, restriction to public actions, and incapability to handle disjunctive beliefs. In this paper, we consider centralized multi-agent epistemic planning from the viewpoint of a third person who coordinates all the agents to achieve the goal. We treat contingent planning, resulting in nonlinear plans. We model private actions and hence handle beliefs, formalized with the multi-agent KD45 logic. We handle static propositional common knowledge, which we call constraints. For such planning settings, we propose a general representation framework where the initial knowledge base (KB) and the goal, the preconditions and effects of actions can be arbitrary KD45n formulas, and the solution is an action tree branching on sensing results. In this framework, the progression of KBs w.r.t. actions is achieved through the operation of belief revision or update on KD45n formulas, that is, higher-order belief revision or update. To support efficient reasoning and progression, we make use of a normal form for KD45n called alternating cover disjunctive formulas (ACDFs). We propose reasoning, revision and update algorithms for ACDFs. Based on these algorithms, adapting the PrAO algorithm for contingent planning from the literature, we implemented a multi-agent epistemic planner called MEPK. Our experimental results show the viability of our approach.

A Factored Approach to Deterministic Contingent Multi-Agent Planning

Collaborative Multi-Agent Planning (MAP) under uncertainty with partial observability is a notoriously difficult problem. Such MAP problems are often modeled as DecPOMDPs, or its qualitative variant, QDec-POMDP, which is essentially a MAP version of contingent planning. The QDecPOMDP model was introduced with the hope that its simpler, non-probabilistic structure will allow for better scalability. Indeed, at least with deterministic actions, the recent IMAP algorithm scales much better than comparable DecPOMDP algorithms (Bazinin and Shani 2018). In this work we suggest a new approach to solving Deterministic QDecPOMDPs based on problem factoring. First, we find a solution to a MAP problem where the results of any observation is available to all agents. This is essentially a single-agent planning problem for the entire team. Then, we project the solution tree into sub-trees, one per agent, and let each agent transform its projected tree into a legal local tree. If all agents succeed, we combine the trees into a valid joint-plan. Otherwise, we continue to explore the space of team solutions. This approach is sound, complete, and as our empirical evaluation demonstrates, scales much better than the IMAP algorithm.

Computing Contingent Plan Graphs using Online Planning

In contingent planning under partial observability with sensing actions, agents actively use sensing to discover meaningful facts about the world. 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. However, the translation may become very large, encumbering the task of the non-deterministic planner. 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 have an exponential width in the number of state variables, but the tree may have 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. Our method also handles non-deterministic domains, by identifying cycles in the plans. We present a set of experiments, showing our approach to scale better than state-of-the-art offline planners.

Remote work: An example of how to identify a downtown-related trend breeze that probably will outlast the COVID-19 crisis

Crises are well known for their ability to create significant economic, social and political changes. In their midst, however, it is often difficult to identify those new trends that will last well beyond the crisis. That can make planning to resolve major problems difficult until the crisis eases or passes. To facilitate useful contingent planning, this paper outlines a process for identifying those trend breezes most likely to endure and be true and impactful trends. It then applies this analytical process to the huge increase in remote working that the COVID-19 pandemic has sparked. The analysis demonstrates that remote work was growing before the crisis, has other trends that reinforce it and that employees and employers have strong non-health reasons to continue a robust level of its use. Moreover, remote work probably will have significant economic impacts on urban, suburban and rural communities long after the pandemic eases. It thrives best in the industries most dependent on creative/knowledge workers and a critical mass of them are now committed to remote working. Employers that refuse to oblige them are likely to have a more difficult time recruiting and retaining such highly skilled and much sought after workers, especially since they are already in short supply.

Knowledge-based programs as succinct policies for partially observable domains

We suggest to express policies for contingent planning by knowledge-based programs (KBPs). KBPs, introduced by Fagin et al. (1995) [32], are high-level protocols describing the actions that the agent should perform as a function of their current knowledge: branching conditions are epistemic formulas that are interpretable by the agent. The main aim of our paper is to show that KBPs can be seen as a succinct language for expressing policies in single-agent contingent planning.KBP are conceptually very close to languages used for expressing policies in the partially observable planning literature: like them, they have conditional and looping structures, with actions as atomic programs and Boolean formulas on beliefs for choosing the execution path. Now, the specificity of KBPs is that branching conditions refer to the belief state and not to the observations.Because of their structural proximity, KBPs and standard languages for representing policies have the same power of expressivity: every standard policy can be expressed as a KBP, and every KBP can be “unfolded” into a standard policy. However, KBPs are more succinct, more readable, and more explainable than standard policies. On the other hand, they require more online computation time, but we show that this is an unavoidable tradeoff. We study knowledge-based programs along four criteria: expressivity, succinctness, complexity of online execution, and complexity of verification.

Human-aware Contingent Planning

Human-aware Contingent Planning

When Fair Isn’t Fair: Understanding Choice Reversals Involving Social Preferences

In settings with uncertainty, tension exists between ex ante and ex post notions of fairness. Subjects in an experiment most commonly select the ex ante fair alternative ex ante and switch to the ex post fair alternative ex post. One potential explanation embraces consequentialism and construes reversals as time inconsistent. Another abandons consequentialism in favor of deontological (rule-based) ethics and thereby avoids the implication that revisions imply inconsistency. We test these explanations by examining contingent planning and the demand for commitment. Our findings suggest that the most common attitude toward fairness involves a time-consistent preference for applying a naive deontological heuristic.

The Future of Tourism in the COVID-19 Era

The SARS-CoV-2 virus that causes the COVID-19 disease is now fully entrenched in the international community, and has devastated society in what might seem to be a parallel universe. The long-term medical, physical, psychological, economic and financial consequences for continents, countries, regions, states, provinces, prefectures, cities, and individuals is not yet known, but the short-term effects do not seem promising or encouraging. One of the most heavily affected industries by COVID-19 is the international travel, tourism demand and hospitality industry, which was one of the world’s largest sources of full-time and casual employment in the pre-COVID-19 era. The future of tourism in the COVID-19 era is presently unknown, but substantial research is required to evaluate how the industry might recover and survive in a new-normal COVID-19 world. The paper presents a discussion of the future of tourism, travel, and hospitality in the time of COVID-19 as a contribution to the industry. The paper presents a discussion of the future of tourism, travel, and hospitality industry. Public and private policy considerations include significant contingent planning to accommodate travel plans and restrictions, personal protection equipment, medical and healthcare requirements, major events, short, medium and long haul domestic and international travel by air and sea, public versus private transportation, hotel accommodation, alternative forms of payment and pre-payment, changes in the tourism and transport industries, changes in tourist behaviour, and potential structural changes.

Comparative criteria for partially observable contingent planning

In contingent planning under partial observability with sensing actions, the solution can be represented as a plan tree, branching on various possible observations. Typically, one seeks a satisfying plan leading to a goal state at each leaf. In many applications, however, one may prefer some satisfying plans to others. We focus on the problem of providing valid comparative criteria for contingent plan trees and graphs, allowing us to compare two plans and decide which one is preferable. We suggest a set of such comparison criteria --- plan simplicity, dominance, and best and worst plan costs. In some cases certain branches of the plan correspond to an unlikely combination of mishaps, and can be ignored, and we provide methods for pruning such unlikely branches before comparing the plan graphs. We explain these criteria, and discuss their validity, correlations, and application to real world problems. We suggest efficient algorithms for computing the comparative criteria. We provide experimental results, showing that plans computed by existing contingent planners can be compared using the suggested criteria.

Utilization Pattern of Kisan Credit Card (KCC) in Baghpat district of Uttar Pradesh

The Kisan Credit Card scheme introduced in the year 1998 with the objective of fulfillment of credit requirement of the farmers in hassle-free manner for raising agricultural production. There has been tremendous increase in number of Kisan Credit Cards in the rural areas during the last five years. Keeping the importance of KCC, the present study was an attempt to assess utility of KCC as perceived by the farmers. The present investigation was carried out in Baghpat district of Utter Pradesh state with a sample of 120 farmers. The study revealed that the utilization of Kisan Credit Card (KCC) by the respondents was observed as maximum in the aspect i.e. increase in agricultural produce followed by increase in income, crop production activities, utilization of credit for vegetable production and allied activities and duration of utilizing credit. it was also found that Utilization of KCC was considerable low in the respect of the components as utilization for crop insurance, change in cropping pattern and diversified farming. The study suggests that Contingent planning for adverse climatic condition should be communicated to farmers well in advance. With a view to enhance the adoption of improved crop production technologies among the farmer’s awareness and training programmes should be conducted in the regard.

The Emergence of the ERP Software Market between Product Innovation and Market Shaping

In this empirical research, the author focuses on the enterprise resource planning (ERP) software market. Based on a contingent perspective of how markets emerge and can be shaped, the author asks the research question of whether the emergence of the ERP market was a necessary, strong, or weak consequence of the product innovation of Systems, Applications & Products in Data Processing (SAP), which was the pioneer innovator in this specific market. This question is answered with a graph theoretical model of contingency and causality in order to measure the causality between events occurring over historical time. In this sense, the research article provides an application of the method proposed by Lehmann-Waffenschmidt. The author finds that the emergence of the ERP software market is contingent and was not predetermined; path dependencies play a big role in the way how this market segment emerged. With respect to both entrepreneurial and economic factors of relevance, the case of the SAP is far from a predetermined success story. The results are relevant for a number of reasons. First, the results indicate that instead of talking about success stories, a new perspective in market shaping can highlight a more realistic way of the contingent nature of entrepreneurial activity and product innovations. Second, the results aim to bridge the gap between marketing and the emergence of markets, as it was indicated as a research gap by recent contributions in marketing science. Third, the introduction of counterfactual events in the business history of SAP indicate a methodological innovation that has not yet been considered by marketing and entrepreneurship scholars, which may be helpful regarding recognizing patterns from the past and also regarding contingent planning for the future.