Efficient approaches for multi-agent planning

Multi-agent planning (MAP) deals with planning systems that reason on long-term goals by multiple collaborative agents which want to maintain privacy on their knowledge. Recently, new MAP techniques have been devised to provide efficient solutions. Most approaches expand distributed searches using modified planners, where agents exchange public information. They present two drawbacks: they are planner-dependent; and incur a high communication cost. Instead, we present two algorithms whose search processes are monolithic (no communication while individual planning) and MAP tasks are compiled such that they are planner-independent (no programming effort needed when replacing the base planner). Our two approaches first assign each public goal to a subset of agents. In the first distributed approach, agents iteratively solve problems by receiving plans, goals and states from previous agents. After generating new plans by reusing previous agents' plans, they share the new plans and some obfuscated private information with the following agents. In the second centralized approach, agents generate an obfuscated version of their problems to protect privacy and then submit it to an agent that performs centralized planning. The resulting approaches are efficient, outperforming other state-of-the-art approaches.This work has been partially supported by MICINN projects TIN2008-06701-C03-03, TIN2011-27652-C03-02 and TIN2014-55637-C2-1-R

Machine learning in hybrid hierarchical and partial-order planners for manufacturing domains

The application of AI planning techniques to manufacturing Systems is being widely deployed for all the tasks involved in the process, from product design to production planning and control. One of these problems is the automatic generation of control sequences for the entire manufacturing system in such a way that final plans can be directly use das the sequential control programs which drive the operation of manufacturing systems. Hybis is a hierarchical and nonlinear planner whose goal is to obtain partially ordered plans at such a level of detail that they can be use das sequential control programs for manufacturing systems. Currently, those sequential control programs are being generated by hand using modelling tools. This document describes a work whose aim is to improve the efficiency of solving problems with Hybis by using machine learning techniques. It implements a deductive learning method that is able to automatically acquire control knowledge (heuristics) by generating bounded explanations of the problem solving episodes. The learning approach builds on Hamlet, a system that learns control knowledge in the form of control rules.This work was partially supported by a grant from the Ministerio de Ciencia y Tecnología through projects TAP1999-0535-C02-02, TIC2001-4936-E, and TIC2002-04146-C05-05.Publicad

Transferring learned control-knowledge between planners

As any other problem solving task that employs search, AI Planning needs heuristics to efficiently guide the problem-space exploration. Machine learning (ML) provides several techniques for automatically acquiring those heuristics. Usually, a planner solves a problem, and a ML technique generates knowledge from the search episode in terms of complete plans (macro-operators or cases), or heuristics (also named control knowledge in planning). In this paper, we present a novel way of generating planning heuristics: we learn heuristics in one planner and transfer them to another planner. This approach is based on the fact that different planners employ different search bias. We want to extract knowledge from the search performed by one planner and use the learned knowledge on another planner that uses a different search bias. The goal is to improve the efficiency of the second planner by capturing regularities of the domain that it would not capture by itself due to its bias. We employ a deductive learning method (EBL) that is able to automatically acquire control knowledge by generating bounded explanations of the problem-solving episodes in a Graphplan-based planner. Then, we transform the learned knowledge so that it can be used by a bidirectional planner.20th International Joint Conferences on Artificial Intelligence (IJCAI-07)Hyderabad, India, 9 - 12 Jan 2007Publicad

On learning control knowledge for a HTN-POP hybrid planner

Proceeding of: First International Conference on Machine Learning and Cybernetics (ICMLC'02), 4-5 Nov. 2002In this paper we present a learning method that is able to automatically acquire control knowledge for a hybrid HTN-POP planner called HYBIS. HYBIS decomposes a problem in subproblems using either a default method or a user-defined decomposition method. Then, at each level of abstraction, it generates a plan at that level using a POCL (Partial Order Causal Link) planning technique. Our learning approach builds on HAMLET, a system that learns control knowledge for a total order non-linear planner (PRODIGY4.0). In this paper, we focus on the operator selection problem for the POP component of HYBIS, which is very important for efficiency purposes.Publicad

A social and emotional model for obtaining believable emergent behaviors

Proceeding of: 13th International Conference, AIMSA 2008, Varna, Bulgaria, September 4-6, 2008This paper attempts to define an emotional model for virtual agents that behave autonomously in social worlds. We adopt shallow modeling based on the decomposition of the emotional state in two qualities: valence (pleasantness or hedonic value) and arousal (bodily activation) and, also, for the agent personality based on the five factors model (openness, conscientiousness, extroversion, agreeableness and neuroticism). The proposed model aims to endow agents with a satisfactory emotional state achieved through the social actions, i.e. the development of social abilities. Psychology characterizes these social abilities for: using the language as a tool (verbal and nonverbal communication), being learned, producing reciprocal reward among the individuals involved in the communication and for depending on the individual features. We have implemented our model in the framework of a computer game, AI-live, to show its validity.Publicad

Plan merging by reuse for multi-agent planning

Multi-Agent Planning deals with the task of generating a plan for/by a set of agents that jointly solve a planning problem. One of the biggest challenges is how to handle interactions arising from agents' actions. The first contribution of the paper is Plan Merging by Reuse, pmr, an algorithm that automatically adjusts its behaviour to the level of interaction. Given a multi-agent planning task, pmr assigns goals to specific agents. The chosen agents solve their individual planning tasks and the resulting plans are merged. Since merged plans are not always valid, pmr performs planning by reuse to generate a valid plan. The second contribution of the paper is rrpt-plan, a stochastic plan-reuse planner that combines plan reuse, standard search and sampling. We have performed extensive sets of experiments in order to analyze the performance of pmr in relation to state of the art multi-agent planning techniques.This work has been partially supported by the MINECO projects TIN2017-88476-C2-2-R, RTC-2016-5407-4, and TIN2014-55637-C2-1-R and MICINN project TIN2011-27652-C03-02

Exploring the relationship between co-creation and satisfaction using QCA

ustomer behavior is one of the key components of value co-creation. Several authors believe that co-creation generates satisfaction. However, few studies exist that focus on that relationship. This study explores the relationship between value co-creation and customer satisfaction in spa services through a fuzzy-set qualitative comparative analysis (fsQCA). QCA analysis allows exploring the relations between the variables. The main contribution of this article is going beyond identifying the concrete co-creation variables that relate to satisfaction. The sample consists of hotel clients that use the spa service.Navarro, S.; Llinares Millán, MDC.; Garzon, D. (2016). Exploring the relationship between co-creation and satisfaction using QCA. Journal of Business Research. 69(4):1336-1339. doi:10.1016/j.jbusres.2015.10.103S1336133969

Determination of the influence of specific building regulations in smart buildings

The automation of domestic services began to be implemented in buildings since the late nineteenth century, and today we are used to terms like ‘intelligent buildings’, ‘digital home’ or ‘domotic buildings’. These concepts tell us about constructions which integrate new technologies in order to improve comfort, optimize energy consumption or enhance the security of users. In conjunction, building regulations have been updated to suit the needs of society and to regulate these new facilities in such structures. However, we are not always sure about how far, from the quantitative or qualitative point of view, legislation should regulate certain aspects of the building activity. Consequently, content analysis is adopted in this research to determine the influence of building regulations in the implementation of new technologies in the construction process. This study includes the analysis of different European regulations, the collection and documentation of such guidelines that have been established and a study of the impact that all of these have had in the way we start thinking an architectural project. The achievements of the research could be explained in terms of the regulatory requirements that must be taken into account in order to achieve a successful implementation of a home automation system, and the key finding has been the confirmation of how the design of smart buildings may be promoted through specific regulatory requirements while other factors, such as the global economic situation, do not seem to affect directly the rate of penetration of home automation in construction

Design Attributes Influencing the Success of Urban 3D Visualizations: Differences in Assessments According to Training and Intention

[EN] The graphic tools most widely used for communicating the design of future urban spaces are 3D visualizations. These virtual images allow graphic designers to manipulate conditions to embellish the final image they present. But, what design attributes are associated with positive assessments? This paper attempts to identify the key design attributes for a successful proposal and observes whether intention (assess the image versus assess the project) and observer training (architect versus non-architect) influence that relationship. A field study was carried out using assessments from 225 individuals. Results show that color, nature, and architecture are fundamental elements in successful proposals. Significant differences in assessments have also been observed according to the training and intentions of the assessorsThis work was supported by Ministerio de Economía y Competitividad (Spain) [grant no. TIN2013-45736-R].Llinares Millán, MDC.; Iñarra Abad, S.; Guixeres Provinciale, J. (2018). Design Attributes Influencing the Success of Urban 3D Visualizations: Differences in Assessments According to Training and Intention. Journal of Urban Technology. https://doi.org/10.1080/10630732.2018.1444873

PLTOOL: a knowledge engineering tool for planning and learning

Artificial intelligence (AI) planning solves the problem of generating a correct and efficient ordered set of instantiated activities, from a knowledge base of generic actions, which when executed will transform some initial state into some desirable end-state. There is a long tradition of work in AI for developing planners that make use of heuristics that are shown to improve their performance in many real world and artificial domains. The developers of planners have chosen between two extremes when defining those heuristics. The domain-independent planners use domain-independent heuristics, which exploit information only from the ‘syntactic’ structure of the problem space and of the search tree. Therefore, they do not need any ‘semantic’ information from a given domain in order to guide the search. From a knowledge engineering (KE) perspective, the planners that use this type of heuristics have the advantage that the users of this technology need only focus on defining the domain theory and not on defining how to make the planner efficient (how to obtain ‘good’ solutions with the minimal computational resources). However, the domain-dependent planners require users to manually represent knowledge not only about the domain theory, but also about how to make the planner efficient. This approach has the advantage of using either better domain-theory formulations or using domain knowledge for defining the heuristics, thus potentially making them more efficient. However, the efficiency of these domain-dependent planners strongly relies on the KE and planning expertise of the user. When the user is an expert on these two types of knowledge, domain-dependent planners clearly outperform domain-independent planners in terms of number of solved problems and quality of solutions. Machine-learning (ML) techniques applied to solve the planning problems have focused on providing middle-ground solutions as compared to the aforementioned two extremes. Here, the user first defines a domain theory, and then executes the ML techniques that automatically modify or generate new knowledge with respect to both the domain theory and the heuristics. In this paper, we present our work on building a tool, PLTOOL (planning and learning tool), to help users interact with a set of ML techniques and planners. The goal is to provide a KE framework for mixed-initiative generation of efficient and good planning knowledge.This work has been partially supported by the Spanish MCyT project TIC2002-04146-C05-05, MEC project TIN2005-08945-C06-05 and regional CAM-UC3M project UC3M-INF-05-016.Publicad