Artificial intelligence tools for academic management: assigning students to academic supervisors

[EN] In the last few years, there has been a broad range of research focusing on how learning should take place both in the classroom and outside the classroom. Even though academic dissertations are a vital step in the academic life of both students, as they get to employ all their knowledge and skills in an original project, there has been limited research on this topic. In this paper we explore the topic of allocating students to supervisors, a time-consuming and complex task faced by many academic departments across the world. Firstly, we discuss the advantages and disadvantages of employing different allocation strategies from the point of view of students and supervisors. Then, we describe an artificial intelligence tool that overcomes many of the limitations of the strategies described in the article, and that solves the problem of allocating students to supervisors. The tool is capable of allocating students to supervisors by considering the preferences of both students and supervisors with regards to research topics, the maximum supervision quota of supervisors, and the workload balance of supervisors.Sanchez-Anguix, V.; Chalumuri, R.; Alberola Oltra, JM.; Aydogan, R. (2020). Artificial intelligence tools for academic management: assigning students to academic supervisors. IATED. 4638-4644. https://doi.org/10.21125/inted.2020.1284S4638464

Heuristic-based approaches for (CP)-nets in negotiation

CP-Nets have proven to be an effective representation for capturing preferences. However, their use in multiagent negotiation is not straightforward. The main reason for this is that CP-Nets capture partial ordering of preferences, whereas negotiating agents are required to compare any two outcomes based on the request and offers. This makes it necessary for agents to generate total orders from their CP-Nets. We have previously proposed a heuristic to generate total orders from a given CP-Net. This paper proposes another heuristic based on Borda count, applies it in negotiation, and compares its performance with the previous heuristic

A baseline for non-linear bilateral negotiations: the full results of the agents competing in ANAC 2014

In the past few years, there is a growing interest in automated negotiation in which software agents facilitate negotiation on behalf of their users and try to reach joint agreements. The potential value of developing such mechanisms becomes enormous when negotiation domain is too complex for humans to find agreements (e.g. e-commerce) and when software components need to reach agreements to work together (e.g. web-service composition). Here, one of the major challenges is to design agents that are able to deal with incomplete information about their opponents in negotiation as well as to effectively negotiate on their users’ behalves. To facilitate the research in this field, an automated negotiating agent competition has been organized yearly. This paper introduces the research challenges in Automated Negotiating Agent Competition (ANAC) 2014 and explains the competition set up and results. Furthermore, a detailed analysis of the best performing five agents has been examined

Unanimously acceptable agreements for negotiation teams in unpredictable domains

A negotiation team is a set of agents with common and possibly also conflicting preferences that forms one of the parties of a negotiation. A negotiation team is involved in two decision making processes simultaneously, a negotiation with the opponents, and an intra-team process to decide on the moves to make in the negotiation. This article focuses on negotiation team decision making for circumstances that require unanimity of team decisions. Existing agent-based approaches only guarantee unanimity in teams negotiating in domains exclusively composed of predictable and compatible issues. This article presents a model for negotiation teams that guarantees unanimous team decisions in domains consisting of predictable and compatible, and alsounpredictable issues. Moreover, the article explores the influence of using opponent, and team member models in the proposing strategies that team members use. Experimental results show that the team benefits if team members employ Bayesian learning to model their teammates’ preferences. 2014 Elsevier B.V. All rights reserved.This research is partially supported by TIN2012-36586-C03-01 of the Spanish government and PROMETEOII/2013/019 of Generalitat Valenciana. Other part of this research is supported by the Dutch Technology Foundation STW, applied science division of NWO and the Technology Program of the Ministry of Economic Affairs; the Pocket Negotiator Project with Grant No. VICI-Project 08075.Sánchez Anguix, V.; Aydogan, R.; Julian Inglada, VJ.; Jonker, C. (2014). Unanimously acceptable agreements for negotiation teams in unpredictable domains. Electronic Commerce Research and Applications. 13(4):243-265. https://doi.org/10.1016/j.elerap.2014.05.002S24326513

Optimal negotiation decision functions in time-sensitive domains

The last two decades have seen a growing interest in automated agents that are able to negotiate on behalf of human negotiators in a wide variety of negotiation domains. One key aspect of a successful negotiating agent is its ability to make appropriate concessions at the right time, especially when there are costs associated with the duration of the negotiation. However, so far, there is no fundamental approach on how much to concede at every stage of the negotiation in such time-sensitive domains. We introduce an efficient solution based on simultaneous search, which is able to select the optimal sequence of offers that maximizes expected payoff, given the agent's beliefs about the opponent. To this end, we show that our approach is consistent with known theoretical results and we demonstrate both its effectiveness and natural properties by applying it to a number of typical negotiation scenarios. Finally, we show in a number of experiments that our solution outperforms other state of the art strategy benchmarks

Deniz: A Robust Bidding Strategy for Negotiation Support Systems

This paper presents the Deniz agent that has been specifically designed to support human negotiators in their bidding. The design of Deniz is done with the criteria of robustness and the availability of small data, due to a small number of negotiation rounds in mind. Deniz’s bidding strategy is based on an existing optimal concession strategy that concedes in relation to the expected duration of the negotiation. This accounts for the small data and small number of rounds. Deniz deploys an adaptive behavior-based mechanism to make it robust against exploitation. We tested Deniz against typical bidding strategies and against human negotiators. Our evaluation shows that Deniz is robust against exploitation and gains statistically significant higher utilities than human test subjects, even though it is not designed specifically to get the highest utility against humans.</p

Artificial Intelligence Techniques for Conflict Resolution

Conflict resolution is essential to obtain cooperation in many scenarios such as politics and business, as well as our day to day life. The importance of conflict resolution has driven research in many fields like anthropology, social science, psychology, mathematics, biology and, more recently, in artificial intelligence. Computer science and artificial intelligence have, in turn, been inspired by theories and techniques from these disciplines, which has led to a variety of computational models and approaches, such as automated negotiation, group decision making, argumentation, preference aggregation, and human-machine interaction. To bring together the different research strands and disciplines in conflict resolution, the Workshop on Conflict Resolution in Decision Making (COREDEMA) was organized. This special issue benefited from the workshop series, and consists of significantly extended and revised selected papers from the ECAI 2016 COREDEMA workshop, as well as completely new contributions