Decentralized action planning in robot coalition using smart contracts.

During a common goal achieving, a coalition of autonomous robots may face a situation that requires prompt decision-making in order to maintain an initially agreed action plan. In this case, it is proposed to use adaptive decentralized planning mechanisms based on the model of socio-inspired self-organization and implemented using the original protocol ofnegotiations between robots. Negotiations are carried out through the execution of smart contracts that process robots' proposalsюThe contracts are storing and distributing in a distributed ledger implemented with the HyperLedger Fabric platform.

DECENTRALIZED PLANNING IN AN INDIAN STATE: AN EXPLORATORY EXERCISE

In India, the idea of decentralized planning gained momentum after the country became independent in 1947 but its rudimentary practice was not completely unknown before 1947. In post-independent India a number of government committees and commissions had recommended for introduction of decentralized planning at different points of time. The most remarkable event in this regard were twin Constitutional amendments which clarified the role of local bodies and institutionalized participation of the people which signaled what is often called a paradigmatic change. The amendments gave decentralized planning constitutional sanction and sanctity, and provided a model of planning for the whole country. In this paper, an attempt has been made to capture the different phases in the evolution of the decentralized planning processes in India as a backdrop and to assess and analyze the experiences of introduction of decentralized planning in one state of the Union of Indian states called West Bengal. It is one of the states where the exercise was done through active participation of people sought to be achieved through institutional structures created in the villages. This paper tries to make use of the available secondary data to arrive at some of the major conclusions and to justify the contentions made. Reference has also been made to some limited field work which was done through village survey. The authors have also highlighted some of the key emerging issues which call for further research. It also seeks to explores what could be the probable lessons the developing countries in general, and India, in particular.

Examples of Applications and Study Cases

The chapter presents examples of applications and study cases of platforms of geospatial decision support systems for national public policies and strategies. The rapid progress of internet with the combination of GIS has paved the ways for web distribution of spatial data. Users can access the spatial data through a Web-GIS website, make thematic maps, and perform all types of spatial queries and analysis. In the context of an increasing emphasis on decentralized planning, the need for collection and dissemination of data at local levels has been increased. Use of the web as a dissemination medium of geographic data in the form of interactive maps can be regarded as a major advancement in digital cartography and opens many new opportunities, such as real-time maps, cheaper dissemination, and decentralized sharing of geographic information.

Integrating Decision Sharing with Prediction in Decentralized Planning for Multi-Agent Coordination under Uncertainty

The performance of decentralized multi-agent systems tends to benefit from information sharing and its effective utilization. However, too much or unnecessary sharing may hinder the performance due to the delay, instability and additional overhead of communications. Aiming to a satisfiable coordination performance, one would prefer the cost of communications as less as possible. In this paper, we propose an approach for improving the sharing utilization by integrating information sharing with prediction in decentralized planning. We present a novel planning algorithm by combining decision sharing and prediction based on decentralized Monte Carlo Tree Search called Dec-MCTS-SP. Each agent grows a search tree guided by the rewards calculated by the joint actions, which can not only be sampled from the shared probability distributions over action sequences, but also be predicted by a sufficiently-accurate and computationally-cheap heuristics-based method. Besides, several policies including sparse and discounted UCT and DIY-bonus are leveraged for performance improvement. We have implemented Dec-MCTS-SP in the case study on multi-agent information gathering under threat and uncertainty, which is formulated as Decentralized Partially Observable Markov Decision Process (Dec-POMDP). The factored belief vectors are integrated into Dec-MCTS-SP to handle the uncertainty. Comparing with the random, auction-based algorithm and Dec-MCTS, the evaluation shows that Dec-MCTS-SP can reduce communication cost significantly while still achieving a surprisingly higher coordination performance.