Software Agents for facilitating collaboration among students in e-learning

Computer supported collaborative learning (CSCL) is one promising technological means to support e-learning over the Internet. However, current CSCL systems work mostly in a passive fashion and do not attempt to take active control of the collaboration. In such systems, it is the responsibility of the participating students to organize and accomplish all the activities of collaborative learning (CL). Students get little assistance from the system during the CL, e.g. the composition of a CL group, the partition of a learning task, the combination of learning outcomes, etc. This paper seeks to actively help and guide students in the CL by software agents. The CSCL over the Internet is first investigated where some challenges for the students while they are taking part in the CL are highlighted. Based on the investigation, a multi-agent architecture to facilitate the CL is proposed. Then, the implementation in one particular CSCL system, LiveNet, is presented and the supports of the agents for the CL are explored. At the final are the conclusions of the paper and some outlooks

Improving Collaborative Learning Using Pervasive Embedded System-Based Multi-Agent Information and Retrieval Framework in Educational Systems

E-learning is a form of Technology SupportedEducation where the medium of instruction is throughDigital Technologies, particularly Computer Technology.An instance is the use of search engines like Google andYahoo, which aid Collaborative Learning. However, thewidespread provision of distributed, semi-structuredinformation resources such as the Web has obviouslybrought a lot of benefits; but it also has a number ofdifficulties. These difficulties include people gettingoverwhelmed by the sheer amount of information available,making it hard for them to filter out the junk andirrelevancies and focus on what is important, and also toactively search for the right information. Also, people easilyget bored or confused while browsing the Web because ofthe hypertext nature of the web, while making it easy to linkrelated documents together, it can also be disorienting. Toalleviate these problems, the Web Information Food ChainModel was introduced. How effective has this been with thedynamic nature of computing technologies? Pervasivecomputing devices enable people to gain immediate accessto information and services anywhere, anytime, withouthaving to carry around heavy and impractical computingdevices. Thus, the bulky PCs become less attractive andbeing slowly eroded with the development of a newgeneration of smart devices like wireless PDAs, smartphones, etc. These embedded devices are characterized bybeing unobtrusively embedded; completely connected;intuitively intelligent; effortlessly portable and mobile; andconstantly on and available. This paper presents the use ofembedded systems and Intelligent Agent-Based WebInformation Food Chain Model in Multi-Agent Informationand Retrieval Framework (IIFCEMAF), to realizing fullpotentials of the internet, for users’ improved system ofcollaborative e-learning in education

Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence

Learning agents that are not only capable of taking tests, but also innovating is becoming a hot topic in AI. One of the most promising paths towards this vision is multi-agent learning, where agents act as the environment for each other, and improving each agent means proposing new problems for others. However, existing evaluation platforms are either not compatible with multi-agent settings, or limited to a specific game. That is, there is not yet a general evaluation platform for research on multi-agent intelligence. To this end, we introduce Arena, a general evaluation platform for multi-agent intelligence with 35 games of diverse logics and representations. Furthermore, multi-agent intelligence is still at the stage where many problems remain unexplored. Therefore, we provide a building toolkit for researchers to easily invent and build novel multi-agent problems from the provided game set based on a GUI-configurable social tree and five basic multi-agent reward schemes. Finally, we provide Python implementations of five state-of-the-art deep multi-agent reinforcement learning baselines. Along with the baseline implementations, we release a set of 100 best agents/teams that we can train with different training schemes for each game, as the base for evaluating agents with population performance. As such, the research community can perform comparisons under a stable and uniform standard. All the implementations and accompanied tutorials have been open-sourced for the community at https://sites.google.com/view/arena-unity/

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table

User evaluation of a market-based recommender system

Recommender systems have been developed for a wide variety of applications (ranging from books, to holidays, to web pages). These systems have used a number of different approaches, since no one technique is best for all users in all situations. Given this, we believe that to be effective, systems should incorporate a wide variety of such techniques and then some form of overarching framework should be put in place to coordinate them so that only the best recommendations (from whatever source) are presented to the user. To this end, in our previous work, we detailed a market-based approach in which various recommender agents competed with one another to present their recommendations to the user. We showed through theoretical analysis and empirical evaluation with simulated users that an appropriately designed marketplace should be able to provide effective coordination. Building on this, we now report on the development of this multi-agent system and its evaluation with real users. Specifically, we show that our system is capable of consistently giving high quality recommendations, that the best recommendations that could be put forward are actually put forward, and that the combination of recommenders performs better than any constituent recommende