This paper advocates a new science of intelligence, one that is holistic, multi-disciplinary, oriented to crucial values as health and well-being and able to contribute to the solution of real-world problems. As a starting point we study the interplay between two research disciplines that until now have been hardly related to each other: Ayurveda and multi-agent systems. We consider some possible results of the cross fertilisation like for instance the application of ayurvedic knowledge to improve the skills of practical reasoning agents.

The Knowledge Engineering Review is an outstanding journal in Computer Science. The guest editors and contributors to this Special Issue are economists. Why is this so? In recent years, there has been a growing dialogue between economists and computer scientists, to our mutual benefit. The Special Issue is devoted to nine papers in which economists survey aspects of the field of agent-based computational economics models, and in some cases report on new findings in several areas of application. As such, we hope it has something to offer both computer scientists and economists.

Social networks occupy a ubiquitous and pervasive place in the life of their users. The substantial amount of content generated and shared by social networking users offers new research opportunities across a wide variety of disciplines, including media and communication studies, linguistics, sociology, psychology, information and computer sciences, or education. This situation, in combination with the continuous growth of social media data, creates an imperative need for content organisation. Thus, large-scale text learning tasks in social environments arise as one of the most relevant problems in machine learning and data mining. Interestingly, social media data pose several challenges due to its sparse, high-dimensional and large-volume characteristics. This survey reviews the field of social media data learning, focusing on classification and clustering techniques, as they are two of the most frequent learning tasks. It reviews not only new techniques that have been developed to tackle the new challenges posed by short-texts, but also how traditional techniques can be adapted to overcome such challenges. Then, open issues and research opportunities for social media data learning are discussed.

One approach to solving planning problems is to compile them to other problems for which powerful off-the-shelf solvers are available; common targets include SAT, CSP, and MILP. Recently, a novel optimization technique has become available: quantum annealing (QA). QA takes as input problem instances of quadratic unconstrained binary optimization (QUBO) problem. Early quantum annealers are now available, though their constraints restrict the types of QUBOs they can take as input. Here, we introduce the planning community to the key steps in compiling planning problems to QA hardware: a hardware-independent step, mapping, and a hardware-dependent step, embedding. After describing two approaches to mapping general planning problems to QUBO, we describe preliminary results from running an early quantum annealer on a parametrized family of hard planning problems. The results show that different mappings can substantially affect performance, even when many features of the resulting instances are similar. We conclude with insights gained from this early study that suggest directions for future work.

In software architectures, architectural design decisions (ADDs) strongly influence the quality of the resulting software system. Wrong decisions lead to low-quality systems and are difficult to repair later on in the development process. As of today, little is known about the impact of certain ADDs for the development of architectures for blockchain-based systems. Thus, it is difficult to predict the outcome of certain ADDs when developing architectures for such systems. In the following, we propose a simulation-based approach for blockchain architectures in which the impact of certain ADDs on certain quality attributes can be simulated. To this end, we first implemented a simulation environment for blockchain architectures. The simulation environment was then used to execute a series of experiments from which we derived a set of hypotheses about the impact of certain ADDs on quality attributes for blockchain architectures. Finally, we tested the hypotheses using statistical analyses and derived an empirical model for blockchain architectures based on the outcome of the analysis. The model can be used by architects to predict the effect of certain decisions in the design of blockchain architectures before implementing them.

This paper holds the material presented at our keynote at the Intelligent Virtual Environments and Virtual Agents Conference, where we analyzed future trends in virtual agent and environment technologies, as applied to the fields of videogames and interactive multimedia. We begin by observing current tendencies in game development projects, both in the market and in the academia, outlining their respective strengths and weaknesses. We follow by describing the aspects of game development that will come to shape the next generation of games, and discuss the requirements for academic projects to achieve the level of quality required to be competitive in this field. In the rest of the paper we analyze the roles, importance, challenges, and research opportunities that a new generation of virtual characters and environments will bring to the world of interactive entertainment.

One of the issues that have garnered little attention, but that is nevertheless important for developing practical robots, is optimal walking conditions like power consumption during walking. The main contribution of this research is to prepare a correct walking pattern for humans who have a problem with their walking and also study the effect of average motion speed on optimal power consumption. In this study, we firstly optimize the stability and minimize the power consumption of the robot during the single support phase using parameter optimization. Our approach is based on the well-known Zero Moment Point method to calculate the stability of the proposed biped robot. Secondly, we performed experiments on healthy male, age 29 years, to analyze human walking by placing 28 markers, attached to anatomical positions and two power plates for a distance of more than one gait cycle at an average speed of 1.23 ± 0.1 m s−1 validate our results for motion analysis of correct walking ability. Our model was continuously validated by comparing the results of our empirical evaluation against the prediction of our model. The errors between experimental test and our prediction were about 4%–11% for the joint trajectories and about 0.2%–0.5% for the ground reaction forces which is acceptable for our prediction. Due to the presented model and optimized issue and predicted path, the robot can move like a person in a way that has maximum stability along with the minimum power consumption. Finally, the robot was able to walk like a specific person that we considered. This study is a case study and also can be generalized to all samples and can perform these procedures to another person’s with different features.

The KADS methodology and its successor, CommonKADS, have gained a reputation for being useful approaches to building knowledge-based systems in a manner which is both systematic and well documented. However, these methods require considerable effort to use them completely. It has been suggested that automated support for KADS or CommonKADS users, in the form of “knowledge engineering workbenches”, could be very useful. These tools would provide computerised assistance to knowledge engineers in organising and representing knowledge, in a similar fashion to the support which CASE tools provide for software engineers. To provide support for KADS or CommonKADS, the workbenches should provide specific support for the modelling techniques recommended by these methods, which are very detailed in the representation and analysis stages of knowledge engineering. A good knowledge engineering workbench should also be easy to use, should be robust and reliable, and should generate output in a presentable format.

This paper reports on an evaluation of two commercially available workbenches for supporting the KADS approach: KADS Tool from ILOG and Open KADS Tool from Bull. This evaluation was carried out by AIAI as part of the CATALYST project, funded by the European Community's ESSI programme, which aimed to introduce CommonKADS to two technology-oriented companies. Information is also presented on two other workbenches: the CommonKADS workbench (which will soon become commercially available) and the VITAL workbench. The results show various strengths and weaknesses in each tool.

In the coming years, scheduling will be an increasingly important activity in the manufacturing and aerospace industries, especially at the US National Aeronautics and Space Administration (NASA). Expert systems have successfully been used to aid in the scheduling function. This paper surveys some of the conflict resolution approaches used in building expert scheduling systems, and then examines the feasibility of using an expert systems approach for scheduling activities and resolving conflicts for experimenters to use NASA Goddard-supported satellites. Recommendations for further reading in expert scheduling systems are given.

Trust between agents in multi-agent systems (MASs) is critical to encourage high levels of cooperation. Existing methods to assess trust and reputation use direct and indirect past experiences about an agent to estimate their future performance; however, these will not always be representative if agents change their behaviour over time.

Real-world distributed networks such as online market places, P2P networks, pervasive computing and the Smart Grid can be viewed as MAS. Dynamic agent behaviour in such MAS can arise from seasonal changes, cheaters, supply chain faults, network traffic and many other reasons. However, existing trust and reputation models use limited techniques, such as forgetting factors and sliding windows, to account for dynamic behaviour.

In this paper, we propose Reacting and Predicting in Trust and Reputation (RaPTaR), a method to extend existing trust and reputation models to give agents the ability to monitor the output of interactions with a group of agents over time to identify any likely changes in behaviour and adapt accordingly. Additionally, RaPTaR can provide an a priori estimate of trust when there is little or no interaction data (either because an agent is new or because a detected behaviour change suggests recent past experiences are no longer representative). Our results show that RaPTaR has improved performance compared to existing trust and reputation methods when dynamic behaviour causes the ranking of the best agents to interact with to change.

The Web search has special characteristics against the desktop search when realized from mobile devices. To establish an improvement within this paradigm, an option is to take into account the context from which the search is developed. To conceptualize the mobile context, we propose the use of ontologies, which will include the device characteristics, environmental conditions and user preferences, among other term conceptualizations. This context definition would be used to determinate the behavior of a word recommendation when searching from mobile devices. As an essential process of creating this context ontology, we have made a real user's evaluation of the ontology terms by means of a survey. This paper shows a brief introduction to the project and focuses mainly on the results obtained in this concept's evaluation.

This paper introduces the Special Issue of The Knowledge Engineering Review devoted to reports arising from selected Technical Forum Groups at the First and Second AgentLink III Agent Technical Forums, held in 2004 and 2005.

Intelligent computing in large-scale systems provides systematic methodologies and tools for building complex inferential systems, which are able to adapt, mine data sets, evolve, and act in a nimble manner within major distributed environments with diverse architectures featuring multiple cores, accelerators, and high-speed networks.

We believe that the papers presented in this special issue ought to serve as a reference for students, researchers, and industry practitioners interested in the evolving, interdisciplinary area of intelligent computing in large-scale systems. We very much hope that readers will find in this compendium new inspiration and ideas to enhance their own research.

We propose an approach to knowledge-based decision-model construction. The domain knowledge is represented in the form of multilevel influence diagrams. At each level, a regular influence diagram representing a decision model may be generated and the influence diagrams between the levels are related through a series of operations. We defined eight such operations and show that these are sufficient at the structural level to construct any target influence diagram in any practical situation. We provide a series of examples drawn from different domains on the use of multilevel influence diagrams for model construction. We also propose suggestions on providing users with guidance that can direct the model-construction procedure towards the best target model.

Research on preferences has significantly increased in recent years, as it involves not only many subproblems to be investigated, such as elicitation, representation, and reasoning, but has also been the target of different research areas, for example, artificial intelligence and databases. In particular, much work has focused on qualitative preferences, because these are closer to the way people express their preferences in comparison with quantitative preferences. Against this background, a large number of approaches have been proposed, associated with heterogeneous areas, so that these approaches are usually just compared with those of the same area. In response, we present in this paper a survey of approaches to qualitative multi-attribute preference reasoning, covering different research areas. We introduce selected approaches that propose different techniques and algorithms, which take as input qualitative multi-attribute preference statements following a particular structure specified by the approach. We analyse each approach in a systematic way and discuss their commonalities and limitations.

This paper considers the problem of generating conflict-free movement schedules for a set of vehicles that are operating simultaneously in a common airspace. In both civilian air traffic management and military air campaign planning contexts, it is crucial that the movements of different vehicles be coordinated so as to avoid collisions and near misses. Our approach starts from a view of airspace management as a 4D resource allocation problem, where the space in which vehicles must maneuver is itself managed as a capacitated resource. We introduce a linear octree representation of airspace capacity to index vector-based vehicle routes and efficiently detect regions of potential conflict. Generalizing the notion of contention-based search heuristics, we next define a scheduling algorithm that proceeds by first solving a relaxed version of the problem to construct a spatial capacity profile (represented as an octree), and then using spatio-temporal regions where demand exceeds capacity to make conflict-avoiding vehicle routing and scheduling decisions. We illustrate the utility of this basic representation and search algorithm in two ways. First, to demonstrate the overall viability of the approach, we present experimental results using data representing a realistically sized air campaign planning domain. Second, we define a more abstract notion of ‘encounter set’, which tolerates some amount of conflict on the assumption that on-board deconfliction processes can take appropriate avoidance maneuvers at execution time, and show that generation of this more abstract form of predictive guidance can be obtained without loss in computational efficiency.

Traditional Route Search aims at finding the path that goes through geographical entities that are relevant to the provided search terms from the start point to the end point. Without constraints, traditional Route Search visiting multiple locations is unreliable because locations may close after a specified time. In this paper, time constraint (operating hours of each location) is drawn into Route Search query in order to make the query more realistic. Two methods are proposed in this paper, namely Route Search for fixed locations (RFix) and Route Search for flexible locations (RFlex). These two queries are different from the existing Route Search query because (1) the end point is not pre-defined and (2) time constraint is involved. Our two proposal queries consider whether the locations are specifically pre-defined by the user or only the location types are specified. In each method, two propositions are presented for pruning expansion branches, which highly improves the performance. Our experiments verified the applicability of RFix and RFlex to solve Route Search queries with time constraint queries.

The First International Conference on Autonomous Agents brought together researchers concerned with implementing systems that perceive and act in dynamic, unpredictable environments, that coordinate interoperation among complementary component capabilities, and that perform significant jobs with a high degree of autonomy.

This paper presents a new ‘Language Independent Recommender Agent’ (LIRA), using information distributed over any text-source pair on the Web about candidate items. While existing review-based recommendation systems learn the features of candidate items and users’ preferences, they do not handle varying perspectives of users on those features. LIRA constructs agents for each user, which run regression algorithms on texts from different sources and builds trust relations. The key advantages of LIRA can be listed as: LIRA does not require reviews from target users, LIRA calculates trust values based on prediction accuracy instead of social connections or rating similarity, LIRA does not require the reviews to come from the same community or peer user group. Since ratings of the reviewers are not necessary for LIRA, we can collect and use reviews from different sources (web pages, professional critiques), as long as we know the corresponding item and source of that text. Since LIRA does not combine text from different sources, texts from different sources are not required to be in the same language. LIRA can utilize text from multiple languages, as long as sources are consistent with their language usage.