Knowledge engineering techniques for automated planning

Formulating knowledge for use in AI Planning engines is currently some-thing of an ad-hoc process, where the skills of knowledge engineers and the tools they use may significantly influence the quality of the resulting planning application. There is little in the way of guidelines or standard procedures, however, for knowledge engineers to use when formulating knowledge into planning domain languages such as PDDL. Also, there is little published research to inform engineers on which method and tools to use in order to effectively engineer a new planning domain model. This is of growing importance, as domain independent planning engines are now being used in a wide range of applications, with the consequence that op-erational problem encodings and domain models have to be developed in a standard language. In particular, at the difficult stage of domain knowledge formulation, changing a statement of the requirements into something for-mal - a PDDL domain model - is still somewhat of an ad hoc process, usually conducted by a team of AI experts using text editors. On the other hand, the use of tools such as itSIMPLE or GIPO, with which experts gen-erate a high level diagrammatic description and automatically generate the domain model, have not yet been proven to be more effective than hand coding. The major contribution of this thesis is the evaluation of knowledge en-gineering tools and techniques involved in the formulation of knowledge. To support this, we introduce and encode a new planning domain called Road Traffic Accidents (RTA), and discuss a set of requirements that we have derived, in consultation with stakeholders and analysis of accident management manuals, for the planning part of the management task. We then use and evaluate three separate strategies for knowledge formulation, encoding domain models from a textual, structural description of require-ments using (i) the traditional method of a PDDL expert and text editor (ii) a leading planning GUI with built in UML modelling tools (iii) an object-based notation inspired by formal methods. We evaluate these three ap-proaches using process and product metrics. The results give insights into the strengths and weaknesses of the approaches, highlight lessons learned regarding knowledge encoding, and point to important lines of research for knowledge engineering for planning. In addition, we discuss a range of state-of-the-art modelling tools to find the types of features that the knowledge engineering tools possess. These features have also been used for evaluating the methods used. We bench-mark our evaluation approach by comparing it with the method used in the previous International Competition for Knowledge Engineering for Plan-ning & Scheduling (ICKEPS). We conclude by providing a set of guide-lines for building future knowledge engineering tools

Agent-Based Algorithms for the Vehicle Routing Problem with Time Windows

Vehicle routing problem s casovymi okny (VRPTW) je jednim z nejdulezitSjSich a nejvice zkou- manych problemu v oblasti dopravy. Matematicky model tohoto problemu vystihuje klicove vlastnosti spolecne cele fadS dalslch dopravmch problemu feSenych v praxi. Jadrem problemu je hledani mnoziny tras zacmajicicli a koncicich v jedinem depu, ktere obsahuji zastavky u mnoziny zakazniku. Pro kazdSho zakazm'ka je pak definovano konkretm' mnozstvf zbozf, jez je tfeba dorucit a casove okno, ve kterem je pozadovano dodani tohoto zbozi. Realne aplikace tohoto problemu jsou zpravidla vyrazne bohatsi, napojene na nadfazene logisticke systemy. KliSoA'ym faktorem pro uspSSne nasazeni odpovldajicich algoritmu je proto jejich fiexibilita vzhledem k dodatecnym rozSuemm zhkladmho matematickeho modelu spojenym s nasazenim v realnem sv§t§. Dalglm podstatnym faktorem je schopnost systemu reagovat na nepfedvidane udalosti jako jsou dopravm zaepy, poruchy, zmgny preferenci zakazniku atd. Multi-agentni systemy reprezentuji architekturu a navrhovy vzor vhodny pro modelovani heterogennlch a dynamickych systemu. Entity v systemu jsou v ramci multi-agentmho mo- delu reprezentovany mnozinou agentil s odpovidajlci'mi vzorci autonommho jako i spolecenskeho chovani. Chovani systemu jako celku pak vyplyva z autonomnich akci...The vehicle routing problem with time windows (VRPTW) is one of the most important and widely studied transportation optimization problems. It abstracts the salient features of numer- ous distribution related real-world problems. It is a problem of finding a set of routes starting and ending at a single depot serving a set of geographically scattered customers, each within a specific time-window and with a specific demand of goods to be delivered. The real world applications of the VRPTW can be very complex being part of higher level sj'^stems i.e. complex supply chain management solutions. For a successful deployment it is impor- tant for these systems to be flexible in terms of incorporating the problem specific side-constraints and problem extensions in an elegant way. Also, employing efficient means of addressing the dy- namism inherent to the execution phase of the relevant operations is vital. The multi-agent systems are an emerging architectm-e with respect to modeling multi-actor heterogenous and dynamic environments. The entities within the system are represented by a set of agents endowed with autonomic as well as social behavioral patterns. The behavior of the system then emerges from their actions and interactions. The autonomic nature of such a model makes it very robust in highly...Katedra softwarového inženýrstvíDepartment of Software EngineeringFaculty of Mathematics and PhysicsMatematicko-fyzikální fakult

A Synthesis of Automated Planning and Model Predictive Control Techniques and its Use in Solving Urban Traffic Control Problem

Most desired applications for planning and scheduling typically have the characteristics of a continuous changing world. Unfortunately, traditional classical planning does not possess this characteristic. This drawback is because most real-world situations involve quantities and numeric values, which cannot be adequately represented in classical planning. Continuous planning in domains that are represented with rich notations is still a great challenge for AI. For instance, changes occurring due to fuel consumption, continuous movement, or environmental conditions may not be adequately modelled through instantaneous or even durative actions; rather these require modelling as continuously changing processes. The development of planning tools that can reason with domains involving continuous and complex numeric fluents would facilitate the integration of automated planning in the design and development of complex application models to solve real world problems. Traditional urban traffic control (UTC) approaches are still not very efficient during unforeseen situations such as road incidents when changes in traffic are requested in a short time interval. For such anomalies, we need systems that can plan and act effectively in order to restore an unexpected road traffic situation into a normal order. In the quest to improve reasoning with continuous process within the UTC domain, we investigate the role of Model Predictive Control (MPC) approach to planning in the presence of mixed discrete and continuous state variables within a UTC problem. We explore this control approach and show how it can be embedded into existing, modern AI Planning technology. This approach preserves the many advantages of the AI Planning approach, to do with domain independence through declarative modelling, and explicit reasoning while leveraging the capability of MPC to deal with continuous processes. We evaluate the possibility of reasoning with the knowledge of UTC structures to optimise traffic flow in situations where a given road within a network of roads becomes unavailable due to unexpected situations such as road accidents. We specify how to augment the standard AI planning engine with the incorporation of MPC techniques into the central reasoning process of a continuous domain. This approach effectively utilises the strengths of search-based and model-simulation-based methods. We create a representation that can be used to capture declaratively, the definitions of processes, actions, events, resources resumption and the structure of the environment in a UTC scenario. This representation is founded on world states modelled by mixed discrete and continuous state variables. We create a planner with a hybrid algorithm, called UTCPLAN that combines both AI planning and MPC approach to reason with traffic network and control traffic signal at junctions within the network. The experimental objective of minimising the number of vehicles in a queue is implemented to validate the applicability and effectiveness of the algorithm. We present an experimental evaluation showing that our approach can provide UTC plans in a reasonable time. The result also shows that the UTCPLAN approach can perform well in dealing with heavy traffic congestion problems, which might result from heavy traffic flow during rush hours

The exploration of unknown environments by affective agents

Tese de doutoramento em Engenharia Informática apresentada à Fac. de Ciências e Tecnologia de CoimbraIn this thesis, we study the problem of the exploration of unknown environments populated with entities by affective autonomous agents. The goal of these agents is twofold: (i) the acquisition of maps of the environment – metric maps – to be stored in memory, where the cells occupied by the entities that populate that environment are represented; (ii) the construction of models of those entities. We examine this problem through simulations because of the various advantages this approach offers, mainly efficiency, more control, and easy focus of the research. Furthermore, the simulation approach can be used because the simplifications that we made do not influence the value of the results. With this end, we have developed a framework to build multi-agent systems comprising affective agents and then, based on this platform, we developed an application for the exploration of unknown environments. This application is a simulated multi-agent environment in which, in addition to inanimate agents (objects), there are agents interacting in a simple way, whose goal is to explore the environment. By relying on an affective component plus ideas from the Belief-Desire-Intention model, our approach to building artificial agents is that of assigning agents mentalistic qualities such as feelings, basic desires, memory/beliefs, desires/goals, and intentions. The inclusion of affect in the agent architecture is supported by the psychological and neuroscience research over the past decades which suggests that emotions and, in general, motivations play a critical role in decision-making, action, and reasoning, by influencing a variety of cognitive processes (e.g., attention, perception, planning, etc.). Reflecting the primacy of those mentalistic qualities, the architecture of an agent includes the following modules: sensors, memory/beliefs (for entities - which comprises both analogical and propositional knowledge representations -, plans, and maps of the environment), desires/goals, intentions, basic desires (basic motivations/motives), feelings, and reasoning. The key components that determine the exhibition of the exploratory behaviour in an agent are the kind of basic desires, feelings, goals and plans with which the agent is equipped. Based on solid, psychological experimental evidence, an agent is equipped in advance with the basic desires for minimal hunger, maximal information gain (maximal reduction of curiosity), and maximal surprise, as well as with the correspondent feelings of hunger, curiosity and surprise. Each one of those basic desires drives the agent to reduce or to maximize a particular feeling. The desire for minimal hunger, maximal information gain and maximal surprise directs the agent, respectively, to reduce the feeling of hunger, to reduce the feeling of curiosity (by maximizing information gain) and to maximize the feeling of surprise. The desire to reduce curiosity does not mean that the agent dislike curiosity. Instead, it means the agent desires selecting actions whose execution maximizes the reduction of curiosity, i.e., actions that are preceded by maximal levels of curiosity and followed by minimal levels of curiosity, which corresponds to maximize information gain. The intensity of these feelings is, therefore, important to compute the degree of satisfaction of the basic desires. For the basic desires of minimal hunger and maximal surprise it is given by the expected intensities of the feelings of hunger and surprise, respectively, after performing an action, while for the desire of maximal information gain it is given by the intensity of the feeling of curiosity before performing the action (this is the expected information gain). The memory of an agent is setup with goals and decision-theoretic, hierarchical task-network plans for visiting entities that populate the environment, regions of the environment, and for going to places where the agent can recharge its battery. New goals are generated for each unvisited entity of the environment, for each place in the frontier of the explored area, and for recharging battery, by adapting past goals and plans to the current world state computed based on sensorial information and on the generation of expectations and assumptions for the gaps in the environment information provided by the sensors. These new goals and respective plans are then ranked according to their Expected Utility which reflects the positive and negative relevance for the basic desires of their accomplishment. The first one, i.e., the one with highest Expected Utility is taken as an intention. Besides evaluating the computational model of surprise, we experimentally investigated through simulations the following issues: the role of the exploration strategy (role of surprise, curiosity, and hunger), environment complexity, and amplitude of the visual field on the performance of the exploration of environments populated with entities; the role of the size or, to some extent, of the diversity of the memory of entities, and environment complexity on map-building by exploitation. The main results show that: the computational model of surprise is a satisfactory model of human surprise; the exploration of unknown environments populated with entities can be robustly and efficiently performed by affective agents (the strategies that rely on hunger combined or not with curiosity or surprise outperform significantly the others, being strong contenders to the classical strategy based on entropy and cost)

Policy-based planning for student mobility support in e-Learning systems

Student mobility in the area of Higher Education (HE) is gaining more attention nowadays. It is one of the cornerstones of the Bologna Process being promoted at both national and international levels. However, currently there is no technical system that would support student mobility processes and assist users in authoring educational curricula involving student mobility. In this study, the problem of student mobility programmes generation based on existing modules and programmes is considered. A similar problem is being solved in an Intelligent Tutoring Systems field using Curriculum generation techniques, but the student mobility area has a set of characteristics limiting their application to the considered problem. One of main limiting factors is that mobility programmes should be developed in an environment with heterogeneous regulations. In this environment, various established routines and regulations are used to control different aspects of the educational process. These regulations can be different in different domains and are supported by different authors independently. In this thesis, a novel framework was developed for generation of student mobility programmes in an environment with heterogeneous regulations. Two core technologies that were coherently combined in the framework are hierarchical planning and policy-based management. The policy-based planner was designed as a central engine for the framework. It extends the functionality of existing planning technologies and provides the means to carry out planning in environments with heterogeneous regulations, specified as policies. The policy-based planner enforces the policies during the planning and guarantees that the resultant plan is conformant with all policies applicable to it. The policies can be supported by different authors independently. Using them, policy authors can specify additional constraints on the execution of planning actions and extend the pre-specified task networks. Policies are enforced during the planning in a coordinated manner: situations when a policy can be enforced are defined by its scope, and the outcomes of policy evaluation are processed according to the specially defined procedures. For solving the problem of student mobility programme generation using the policy-based planner, the planning environment describing the student mobility problem area was designed and this problem was formalised as a planning task. Educational processes valid throughout the HE environment were formalised using Hierarchical Task Network planning constructs. Different mobility schemas were encoded as decomposition methods that can be combined to construct complex mobility scenarios satisfying the user requirements. New mobility programmes are developed as detailed educational processes carried out when students study according to these programmes. This provides the means to model their execution in the planning environment and guarantee that all relevant requirements are checked. The postponed policy enforcement mechanism was developed as an extension of the policy-based planner in order to improve the planning performance. In this mechanism, future dead-ends can be detected earlier during the planning using partial policy requests. The partial policy requests and an algorithm for their evaluation were introduced to examine policies for planning actions that should be executed in the future course of planning. The postponed policy enforcement mechanism was applied to the mobility programme generation problem within the descending policy evaluation technique. This technique was designed to optimise the process of programme components selection. Using it, policies for different domains can be evaluated independently in a descending order, gradually limiting the scope for the required component selection. The prototype of student mobility programme generation solution was developed. Two case studies were used to examine the process of student mobility programmes development and to analyse the role of policies in this process. Additionally, four series of experiments were carried out to analyse performance gains of the descending policy evaluation technique in planning environments with different characteristics

Effects of Aquatic Exercise on Executive Function in the Aging Population

Neurocognitive decline, including Alzheimer\u27s Disease and other forms of dementia, is considered to be the world\u27s fastest growing disease (Alzheimer\u27s Association, 2011). Due to this escalation, research focused on determining causes, accelerants, impeding factors, and preventative strategies has become a focus of interest within the field. One of the principal points of study is the role that exercise plays in the maintenance or fortification against neurocognitive decline. Though there is a robust library of research focused on the effects of land-based exercise on cognitive function, currently there is no research that discusses the impact of aquatic-based exercise on these parameters.;This mixed method study focused on identifying the effects of a 10-week aquatic exercise intervention on parameters of executive function in individuals aged 60 years or older. Additional research questions targeted changes in cardiovascular fitness, wellness, and psychosocial barriers as well as behavior change in relation to the incorporation of adult education, and accessibility to exercise. Thirty-four volunteers between the ages of 60 and 90 years were recruited for this study. The control group agreed to not alter their physical activity status while the intervention group took part in a 10-week aquatic exercise program employing progressive overload and adult education concepts. At the conclusion of the intervention, all participants (n = 34) returned for their physiological, psychosocial, and cognitive post assessments.;The findings revealed that the aquatic exercise intervention did not have global effects on cognitive function or physiological parameters. However, a statistically significant (p = 0.014) change favoring the intervention group was found for the spatial working memory (SWM) between errors score. Qualitative and quantitative data converged to denote no global change to executive function while displaying improvements in cognitive parameters aligned with SWM. Statistically significant positive changes were observed in the DBP (p = 0.014) favoring the intervention group, however results from the 6-minute walk test as well as SBP and RHR only displayed positive trends without reaching statistical significance.;In relation to psychosocial mediators, no statistically significant interactions between the intervention and control groups over time were found. Initial survey results revealed very few perceived barriers, high motivation, sound social support, and high self-efficacy from both the intervention and control groups presenting a potential ceiling effect upon post-test findings. A statistically significant group effect in the control group was noted for social support showing a perceived reduction in social support. Qualitative data corroborated these findings for the intervention group with predominantly positive, voluminous responses in reference to all psychosocial mediators discussed. Barriers were accounted for yet traversed with solid coping strategies; motivation was high within multiple factors producing great motive for program commencement and continuation; self-efficacy was positively perpetuated throughout the course of the intervention via health outcomes and ability levels; and social support was strong through multiple cohort channels.;The exercise intervention was built around an adult education framework consisting of (1) finding motivation to begin exercise, (2) begin integrating exercise that fits into individual lifestyles, and (3) maintain and gain on all fitness parameters. It seems that through the information provided by the focus group participants that a behavior change did occur for the vast majority of the intervention group. With 86% of the attendees devising a plan for continuation of physical activity in conjunction with multiple variations in lifestyle changes and benefit recognition, it seems that the intervention group may be newly initiated chronic exercisers.;In conclusion, this study revealed that aquatic exercise does positively affect selective components of executive function, cardiovascular fitness, and wellness. Whereas psychosocial mediators did not show improvement, the intervention groups\u27 maintenance of a positive association with these mediators following a 10-week aquatic exercise intervention is encouraging. Additionally, with the allowance of active participation in exercise via the aquatic medium, participants\u27 accessibility to exercise was promoted while the perceived improvements in physical ability endorsed a behavior change towards improved overall physical activity levels. (Abstract shortened by UMI.)