Progress in AI Planning Research and Applications

Planning has made significant progress since its inception in the 1970s, in terms both of the efficiency and sophistication of its algorithms and representations and its potential for application to real problems. In this paper we sketch the foundations of planning as a sub-field of Artificial Intelligence and the history of its development over the past three decades. Then some of the recent achievements within the field are discussed and provided some experimental data demonstrating the progress that has been made in the application of general planners to realistic and complex problems. The paper concludes by identifying some of the open issues that remain as important challenges for future research in planning

Online Planner Selection with Graph Neural Networks and Adaptive Scheduling

Automated planning is one of the foundational areas of AI. Since no single planner can work well for all tasks and domains, portfolio-based techniques have become increasingly popular in recent years. In particular, deep learning emerges as a promising methodology for online planner selection. Owing to the recent development of structural graph representations of planning tasks, we propose a graph neural network (GNN) approach to selecting candidate planners. GNNs are advantageous over a straightforward alternative, the convolutional neural networks, in that they are invariant to node permutations and that they incorporate node labels for better inference. Additionally, for cost-optimal planning, we propose a two-stage adaptive scheduling method to further improve the likelihood that a given task is solved in time. The scheduler may switch at halftime to a different planner, conditioned on the observed performance of the first one. Experimental results validate the effectiveness of the proposed method against strong baselines, both deep learning and non-deep learning based. The code is available at \url{https://github.com/matenure/GNN_planner}.Comment: AAAI 2020. Code is released at https://github.com/matenure/GNN_planner. Data set is released at https://github.com/IBM/IPC-graph-dat

Salience and Social Choice

The axioms of expected utility and discounted utility theory have been tested extensively. In contrast, the axioms of social welfare functions have only been tested in a few questionnaire studies involving choices between hypothetical income distributions. In this note, we conduct a controlled experiment with 100 subjects in the role of social planners that tests five fundamental properties of social welfare functions to provide a basic test of cardinal social choice theory. We find that four properties of the standard social welfare functions tested are systematically violated, producing an Allais paradox, a common ratio effect, a framing effect, and a skewness effect in social choice. We also develop a model of salience based social choice which predicts these systematic deviations and highlights the close relationship between these anomalies and the classical paradoxes for risk and time

A new paradigm for deep sustainability: biourbanism

Biourbanism introduces new conceptual and planning models for a new kind of city, valuing social and economical regeneration of the built environment through developing and healthy communities. Thus, it combines technical aspects, such as zero-emission, energy efficiency, information technology, etc. and the promotion of social sustainability and human well being. In effect, this new paradigm endorses principles of geometrical coherence, Biophilic design, BioArchitecture, Biomimesis, etc. in practices of design and also new urban policies and, especially Biopolitics to promote urban revitalization by ensuring that man-made changes do not have harmful effects to humans. Green city standards start inside the designs of each building and continue either in unbuilt spaces surrounding buildings or inside complex infrastructural networks, connecting buildings and people. The proposed presentation should illustrate how new exciting developments recently, such as fractals, complexity theory, evolutionary biology and artificial intelligence are interrelated and constantly stimulate interaction between human beings and the surrounding environment. New Biophilic solutions in designs of buildings have been proved as attractive opportunities for new markets of housing. Thus, some new infrastructural projects start embracing Biophilic advanced solutions which finally aim at energy efficiency and optimal performance. As parallel activity we can now see emerging new innovative monitoring systems of building health not only in small scale, but also in large scale buildings, such as rail stations, for example, and commercial centres or even sometimes entire educational complexes integrated to new infrastructural projects. Some important case studies are going to be presented; they have been analysed and evaluated by Biourbanism and Biophilia principles and applied methods of design

PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planning

Many planning applications involve complex relationships defined on high-dimensional, continuous variables. For example, robotic manipulation requires planning with kinematic, collision, visibility, and motion constraints involving robot configurations, object poses, and robot trajectories. These constraints typically require specialized procedures to sample satisfying values. We extend PDDL to support a generic, declarative specification for these procedures that treats their implementation as black boxes. We provide domain-independent algorithms that reduce PDDLStream problems to a sequence of finite PDDL problems. We also introduce an algorithm that dynamically balances exploring new candidate plans and exploiting existing ones. This enables the algorithm to greedily search the space of parameter bindings to more quickly solve tightly-constrained problems as well as locally optimize to produce low-cost solutions. We evaluate our algorithms on three simulated robotic planning domains as well as several real-world robotic tasks.Comment: International Conference on Automated Planning and Scheduling (ICAPS) 202

Object Action Complexes as an Interface for Planning and Robot Control

Abstract — Much prior work in integrating high-level artificial intelligence planning technology with low-level robotic control has foundered on the significant representational differences between these two areas of research. We discuss a proposed solution to this representational discontinuity in the form of object-action complexes (OACs). The pairing of actions and objects in a single interface representation captures the needs of both reasoning levels, and will enable machine learning of high-level action representations from low-level control representations. I. Introduction and Background The different representations that are effective for continuous control of robotic systems and the discrete symbolic AI presents a significant challenge for integrating AI planning research and robotics. These areas of research should be abl