Eva Onaindia

On the design of individual and group recommender systems for tourism

Research highlights? Recommender system able to provide recommendations for groups of tourists. ? Group members must be equally satisfied with the recommendation. ? Two techniques are explored: intersection and aggregation. ? Intersection obtains better results, it brings together the preferences of all group members. This paper presents a recommender system for tourism based on the tastes of the users, their demographic classification and the places they have visited in former trips. The system is able to offer recommendations for a single user or a group of users. The group recommendation is elicited out of the individual personal recommendations through the application of mechanisms such as aggregation and intersection. The elicitation mechanism is implemented as an extension of e-Tourism, a user-adapted tourism and leisure application whose main component is the Generalist Recommender System Kernel (GRSK), a domain-independent taxonomy-driven recommender system.

samap: An user-oriented adaptive system for planning tourist visits

In this paper, we present samap, whose goal is to build a software tool to help different people visit different cities. This tool integrates modules that dynamically capture user models, determine lists of activities that can provide more utility to a user given the past experience of the system with similar users, and generates plans that can be executed by the user. This system is intended to work in portable devices (mobile phones, PDAs, etc.,) with internet connection. In this paper, we describe the architecture, the knowledge model that is shared among components using an ontology, and the three components of the tool: user module, case-based module and planning module.

e-Tourism: a tourist recommendation and planning application

e-Tourism is a tourist recommendation and planning application to assist users on the organization of a leisure and tourist agenda. First, a recommender system offers the user a list of the city places that are likely of interest to the user. This list takes into account the user demographic classification, the user likes in former trips and the preferences for the current visit. Second, a planning module schedules the list of recommended places according to their temporal characteristics as well as the user restrictions; that is the planning system determines how and when to perform the recommended activities. This is a very relevant feature that most recommender systems lack as it allows the user to have the list of recommended activities organized as an agenda, i.e. to have a totally executable plan.

A Group Recommender System for Tourist Activities

This paper introduces a method for giving recommendations of tourist activities to a group of users. This method makes recommendations based on the group tastes, their demographic classification and the places visited by the users in former trips. The group recommendation is computed from individual personal recommendations through the use of techniques such as aggregation, intersection or incremental intersection. This method is implemented as an extension of the e-Tourism tool, which is a user-adapted tourism and leisure application, whose main component is the Generalist Recommender System Kernel (GRSK) , a domain-independent taxonomy-driven search engine that manages the group recommendation.

FMAP: Distributed cooperative multi-agent planning

This paper proposes FMAP (Forward Multi-Agent Planning), a fully-distributed multi-agent planning method that integrates planning and coordination. Although FMAP is specifically aimed at solving problems that require cooperation among agents, the flexibility of the domain-independent planning model allows FMAP to tackle multi-agent planning tasks of any type. In FMAP, agents jointly explore the plan space by building up refinement plans through a complete and flexible forward-chaining partial-order planner. The search is guided by hDTG, a novel heuristic function that is based on the concepts of Domain Transition Graph and frontier state and is optimized to evaluate plans in distributed environments. Agents in FMAP apply an advanced privacy model that allows them to adequately keep private information while communicating only the data of the refinement plans that is relevant to each of the participating agents. Experimental results show that FMAP is a general-purpose approach that efficiently solves tightly-coupled domains that have specialized agents and cooperative goals as well as loosely-coupled problems. Specifically, the empirical evaluation shows that FMAP outperforms current MAP systems at solving complex planning tasks that are adapted from the International Planning Competition benchmarks.

Preference elicitation techniques for group recommender systems

A key issue in group recommendation is how to combine the individual preferences of different users that form a group and elicit a profile that accurately reflects the tastes of all members in the group. Most Group Recommender Systems (GRSs) make use of some sort of method for aggregating the preference models of individual users to elicit a recommendation that is satisfactory for the whole group. In general, most GRSs offer good results, but each of them have only been tested in one application domain. This paper describes a domain-independent GRS that has been used in two different application domains. In order to create the group preference model, we select two techniques that are widely used in other GRSs and we compare them with two novel techniques. Our aim is to come up with a model that weighs the preferences of all the individuals to the same extent in such a way that no member in the group is particularly satisfied or dissatisfied with the final recommendations.

Context-Aware Multi-Agent Planning in intelligent environments

A system is context-aware if it can extract, interpret and use context information and adapt its functionality to the current context of use. Multi-agent planning generalizes the problem of planning in domains where several agents plan and act together, and share resources, activities, and goals. This contribution presents a practical extension of a formal theoretical model for Context-Aware Multi-Agent Planning based upon an argumentation-based defeasible logic. Our framework, named CAMAP, is implemented on a platform for open multi-agent systems and has been experimentally tested, among others, in applications of ambient intelligence in the field of health-care. CAMAP is based on a multi-agent partial-order planning paradigm in which agents have diverse abilities, use an argumentation-based defeasible contextual reasoning to support their own beliefs and refute the beliefs of the others according to their context knowledge during the plan search process. CAMAP shows to be an adequate approach to tackle ambient intelligence problems as it gathers together in a single framework the ability of planning while it allows agents to put forward arguments that support or argue upon the accuracy, unambiguity and reliability of the context-aware information.

Automatic generation of temporal planning domains for e-learning problems

AI Planning & Scheduling techniques are being widely used to adapt learning paths to the special features and needs of students both in distance learning and lifelong learning environments. However, instructors strongly rely on Planning & Scheduling experts to encode and review the domains for the planner/scheduler to work. This paper presents an approach to automatically extract a fully operational HTN planning domain and problem from a learning objects repository without requiring the intervention of any planning expert, and thus enabling an easier adoption of this technology in practice. The results of a real experiment with a small group of students within an e-Learning private company in Spain are also shown.

Planning and scheduling in an e-learning environment. A constraint-programming-based approach

AI planning techniques offer very appealing possibilities for their application to e-learning environments. After all, dealing with course designs, learning routes and tasks keeps a strong resemblance with a planning process and its main components aimed at finding which tasks must be done and when. This paper focuses on planning learning routes under a very expressive constraint programming approach for planning. After presenting the general planning formulation based on constraint programming, we adapt it to an e-learning setting. This requires to model learners profiles, learning concepts, how tasks attain concepts at different competence levels, synchronisation constraints for working-group tasks, capacity resource constraints, multi-criteria optimisation, breaking symmetry problems and designing particular heuristics. Finally, we also present a simple example (modelled by means of an authoring tool that we are currently implementing) which shows the applicability of this model, the use of different optimisation metrics, heuristics and how the resulting learning routes can be easily generated.

Temporal Reasoning in REAKT: An Environment for Real-Time Knowledge-Based Systems

Temporal representation and reasoning, as the ability of reasoning about temporal data, representing past, current and expected application states, is an important function to be accomplished by Real-Time Knowledge-Based Systems RTKBS, since these systems are usually applied in dynamic time-dependent problem domains. However, this feature is not completely nor usually addressed in current RTKBS tools. In this paper, a RTKBS architecture is presented, with special emphasis on its temporal reasoning function, which is integrated in a RTKBS environment with a multi-agent blackboard architecture. Representation and management of temporal data, representing past, current and future problem states and reasoning processes within these contexts are detailed.