Program
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- Angerona - A flexible Multiagent Framework for Knowledge-based Agents
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We present the Angerona framework for the implementation of knowledge-based agents with a strong focus on flexibility, extensibility and compatibility with diverse knowledge representation formalisms.
Its basis is a flexible plug-in architecture for the mental state of an agent as well as for the agent cycle.
Different knowledge representation formalisms can be used within one agent and different agents in the same system can be based on different agent architectures and can use different knowledge representation formalisms.
Partially instantiated plug-ins define sub-frameworks for, e.\,g., the development of BDI agents and variants thereof.
The knowledge representation plug-ins are tightly coupled to the Tweety library for knowledge representation, which provides of-the-shelf use of diverse knowledge representation formalisms and a framework for the implementation of additional ones.
Angerona already contains several partial and complete instantiations that implement several approaches from the literature.
Since Angerona and Tweety are open source, these can be easily used and extended.
Angerona also features an environment plug-in for communicating agents and a flexible GUI to monitor the simulation and the agents, particularly including the inspection of the dynamics of their mental states.
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Priority-based Merging Operator without Distance Measures
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This paper proposes a refinement of the PS-Merge merging operator, which is an alternative merging approach that employs the notion of partial satisfiability rather than the usual distance measures. Our approach will add to PS-Merge a mechanism to deal with a kind of priority based on the quantity of information of the agents. We will refer to the new operator as Pr-Merge. We will also analyze its logical properties as well its complexity by conceiving an algorithm with a distinct strategy from that presented for PS-Merge.
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A Study on the Influence of the Number of MTurkers on the Quality of the Aggregate Output
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Recent years have seen an increased interest in crowdsourcing as a way of obtaining information from a large group of workers at a reduced cost. In general, there are arguments for and against using multiple workers to perform a task. On the positive side, multiple workers bring different perspectives to the process, which may result in a more accurate aggregate output since biases of individual judgments might offset each other. On the other hand, a larger population of workers is more likely to have a higher concentration of poor workers, which might bring down the quality of the aggregate output.
In this paper, we empirically investigate how the number of workers on the crowdsourcing platform Amazon Mechanical Turk influences the quality of the aggregate output in a content-analysis task. We find that both the expected error in the aggregate output as well as the risk of a poor combination of workers decrease as the number of workers increases. Moreover, our results show that restricting the population of workers to up to the overall top 40% workers is likely to produce more accurate aggregate outputs, whereas removing up to the overall worst 40% workers can actually make the aggregate output less accurate. We find that this result holds due to top-performing workers being consistent across multiple tasks, whereas worst-performing workers tend to be inconsistent. Our results thus contribute to a better understanding of, and provide valuable insights into, how to design more effective crowdsourcing processes. -
Deliberative Argumentation for Service Provision in Smart Environments (short)
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In this paper, we introduce an inquiry dialogue approach for supporting decision making in a smart environment setting. These inquiry dialogues have as topic either agreement atoms or agreement rules, which capture services in a smart environment. These services are provided and supported by three rational agents with different roles: Environment Agent, Activity Agent and Coach Agent. These three agents have different capabilities and represent different data sources; however, they have to collaborate in order to deliver services in a smart environment.
The knowledge base of each agent is captured by extended logic programs. Therefore, the construction of arguments is supported by the Well-Founded Semantics. The outcome of the inquiry dialogues is supported by well-known argumentation semantics. -
Facilitating Multi-Agent Coalition Formation through Cooperation in Self-Interested Environments
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The issue of collaboration amongst agents in a multi-agent system (MAS) represents a challenging research problem. In this paper we focus on a form of cooperation known as coalition formation. More specifically we consider a problem domain defined by a competitive market-place, where self-interested agents must cooperate by forming a coalition in order to complete a task. Agents must reach a consensus on both the monetary amount to charge for completion of a task as well as the distribution of the required workload. The problem is further complicated because respective subtasks have various degrees of difficulty and each agent is uncertain of the payment another agent requires for performing specific subtasks. These complexities coupled with the self-interested nature of agents can inhibit or even prevent the formation of coalitions in such a real-world setting.
As a solution a novel auction-based protocol called ACCORD is proposed. ACCORD manages real-world complexities by promoting the adoption of cooperative behaviour amongst agents. Through extensive empirical analysis we demonstrate that cooperative and fair behaviour is dominant and any agents deviating from this behaviour suffer a degradation in performance. -
Auction-Based Dynamic Task Allocation for Foraging with a Cooperative Robot Team
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Many application domains require search and retrieval, which is also known in the robotic domain as foraging. An example domain is search and rescue where a disaster area needs to be explored and transportation of survivors to a safe area needs to be arranged. Performing these tasks by more than one robot increases performance if tasks are allocated efficiently. In this paper, we study the Multi-Robot Task Allocation (MRTA) problem in the foraging domain. We assume that a team of robots is cooperatively searching for targets of interest in an environment which need to be retrieved and brought back to a home base. We look at a more general foraging problem than is typically studied where coordination also requires to take temporal constraints into account. As usual, robots have no prior knowledge about the location of targets, but in addition need to deliver targets to the home base in a specific order. This significantly increases the complexity of a foraging problem. We use a graph-based model to analyse the problem and the dynamics of allocating exploration and retrieval tasks. Our main contribution is an extension of auction-based approaches to deal with dynamic foraging task allocation where not all tasks are initially known. We use the Blocks World for Teams (BW4T) simulator to evaluate the proposed approach.
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Distributed Deliberation on Direct Help in Agent Teamwork
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This paper explores how the members of an agent team can jointly deliberate on providing direct help to each other with an intended benefit to team performance. By direct help we mean assistance between teammates that is initiated by them as need arises, rather than being imposed by the general organization of the team or by a centralized decision. The deliberation starts with a request for help in some approaches and with an offer of help in others; it is typically effected through a bidding protocol. We examine the existing principles and designs of help deliberation and propose a new protocol, which refines and combines two existing versions into one. The new protocol allows an agent to initiate help deliberation by either a request or an offer, and to simultaneously engage in both providing and receiving assistance. We demonstrate its potential performance gains over the previous versions through simulation experiments.
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Computing coalitions in Multiagent Systems, A contextual reasoning approach -
Ants in the OCEAN: Modulating Agents with Personality for Planning with Humans
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This work introduces a prototype that demonstrates the idea of using a psychological theory of personality types known as the Five-Factor Model (FFM) in planning for human-agent teamwork scenarios. FFM is integrated into the BDI model of agency leading to variations in the interpretation of inputs, the decision-making process and the generation of outputs. This is demonstrated in a multi-agent simulation, further, it is outlined how these variations can be used for the planning process in collaborative settings.
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Comparison of Task-Allocation Algorithms in Frontier-Based Multi-Robot Exploration (short)
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In this paper, we address the problem of efficient allocation of the navigational goals in the multi-robot exploration of unknown environment, where possible goal candidates are selected from a set of locations at the border of already explored and unexplored area of the environment. These candidates are repeatedly determined as the environment is explored and to efficiently utilized available robots in the team, the candidates have to be distributed to the particular exploring units. The distribution of the goals is formulated as a multi-agent task-allocation problem. We consider five state-of-the-art algorithms in this context and compare their performance in several scenarios. These algorithms have different computational requirements, and therefore, their performance depends on the available resources. In robotics, the mission performance is usually measured in a practical deployment; however, in such a case, a simpler and faster algorithm can perform better because of limited on-board computational power, and therefore, such results and comparisons are valid to the particular system and they are not generalizable. We propose an evaluation methodology that allows to study exploration strategies in a precisely defined computational environment and which does not depend on the available computational resources. Based on this evaluation framework, a comparison of the selected exploration strategies is reported.
A multi-robot exploration method for conjunct environments with a systematic return procedure-
A Framework for Epistemic Gossip Protocols
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In this paper we describe the implementation of a framework for the automated modelling and evaluation of epistemic gossip protocols. We consider gossip protocols which aim to spread information within a network of agents by means of private pairwise communications between the agents. This tool is based on the theoretical framework for epistemic gossip protocols presented in \cite{attamahetal2014}. We introduce an easily accessible language for describing epistemic gossip protocols in a program-like manner. And we introduce an interpreter for this language, together with a model generator and model checker for the automated construction of a dynamic model of the described protocol. Given a high level description, the framework tool outputs some key dynamic properties of the described protocol, thus facilitating the process of protocol design and planning. We conclude the paper with some experimental results for some epistemic gossip protocols.
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Synthesis with Rational Environments
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Synthesis is the automated construction of a system from its specification. The system has to satisfy its specification in all possible environments. The environment often consists of agents that have objectives of their own. Thus, it makes sense to soften the universal quantification on the behavior of the environment and take the objectives of its underlying agents into an account. Fisman et al. introduced rational synthesis: the problem of synthesis in the context of rational agents. The input to the problem consists of temporal-logic formulas specifying the objectives of the system and the agents that constitute the environment, and a solution concept (e.g., Nash equilibrium). The output is a profile of strategies, for the system and the agents, such that the objective of the system is satisfied in the computation that is the outcome of the strategies, and the profile is stable according to the solution concept; that is, the agents that constitute the environment have no incentive to deviate from the strategies suggested to them.
In this paper we continue to study rational synthesis. First, we suggest an alternative definition to rational synthesis, in which the agents are rational but not cooperative. In the non-cooperative setting, one cannot assume that the agents that constitute the environment take into account the strategies suggested to them. Accordingly, the output is a strategy for the system only, and the objective of the system has to be satisfied in all the compositions that are the outcome of a stable profile in which the system follows this strategy. We show that rational synthesis in this setting is 2ExpTime-Complete, thus it is not more complex than traditional synthesis or cooperative rational synthesis. Second, we study a richer specification formalism, where the objectives of the system and the agents are not Boolean but quantitative. In this setting, the goal of the system and the agents is to maximize their outcome. The quantitative setting significantly extends the scope of rational synthesis, making the game-theoretic approach much more relevant. -
Compliance Games (short)
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In this paper we analyze compliance games, which are games induced by agent-labeled Kripke structures, goal formulas in the language of CTL and behavioral constraints. In compliance games, players are rewarded for achieving their goals while complying to social laws, and punished for non-compliance. Design of these games is an attempt at capturing notion of sanctions and incentivizing agents to be compliant. We analyze solution concepts and properties of compliance games, study the connection between underlying logical framework and their properties, and provide short analysis of key computational problems.
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MORE: Merged Opinions Reputation Model
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Reputation is generally defined as the opinion of a group on an aspect of a thing. This paper presents a reputation model that follows a probabilistic modelling of opinions based on three main concepts: (1) the value of an opinion decays with time, (2) the reputation of the opinion source impacts the reliability of the opinion, and (3) the certainty of the opinion impacts its weight with respect to other opinions. Furthermore, the model is flexible with its opinion sources: it may use explicit opinions or implicit opinions that can be extracted from agent behaviour in domains where explicit opinions are sparse. We illustrate the latter with an approach to extract opinions from behavioural information in the sports domain, focusing on football in particular. One of the uses of a reputation model is predicting behaviour. We take up the challenge of predicting the behaviour of football teams in football matches, which we argue is a very interesting yet difficult approach for evaluating the model.
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Affordance-Based Interaction Design for Agent-Based Simulation Models
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When designing and implementing an Agent-Based Simulation model
a major challenge is to formulate the interactions between agents and between agents and their environment. In this contribution we present an approach for capturing agent-environment interactions based on the ``affordance'' concept. Originated in ecological psychology, affordances represent relations between environmental objects and potential actions that an agent may perform with those objects and thus offer a higher abstraction level for dealing with potential interaction.
Our approach has two elements: a methodology for using the affordance concept to identify interactions and secondly, a suggestion for integrating affordances into agents' decision making. We illustrate our approach indicating an agent-based model of after-earthquake behavior. -
A Trust-based Situation Awareness Model
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Trust is a social phenomenon that impacts the situation awareness of individuals and indirectly their decision-making. However, most of the existing computational models of situation awareness do not take interpersonal trust into account. Contrary to those models, this study introduces a computational, agent-based situation awareness model incorporating trust to enable building more human-like decision making tools. To illustrate the proposed model, a simulation case study has been conducted in the airline operation control domain. According to the results of this study, the trustworthiness of information sources had a significant effect on airline operation controller's situation awareness.
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Decision Making in Agent-Based Models (short)
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Agent-Based Models (ABM) are being increasingly applied to the study of a wide range of social phenomena, often putting the focus on the macroscopic patterns that emerge from the interaction of a number of agents programmed to behave in a plausible manner. This agent behavior, however, is all too often encoded as a small set of rules that produces a somewhat simplistic behavior. In this short paper, we propose to explore the impact of decision-making processes on the outcome of simulations, and introduce a type of agent that uses a more systematic and principled decision-making approach, based on casting the simulation environment as a Markov Decision Process. We compare the performance of this type of agent to that of more simplistic agents on a simple ABM simulation, and examine the interplay between the decision-making mechanism and other relevant simulation parameters such as the distribution and scarcity of resources. Our preliminary findings show that our novel agent outperforms the rest of agents, and, more generally, that the process of decision-making needs to be acknowledged as a first-class parameter of ABM simulations with a significant impact on the simulation outcome.
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The multi-agent cooperation for optimizing the weight of electrical aircraft harnesses
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This paper deals with the minimization of the weight of the aircraft electrical system. Because of the technological advances that are spreading, the electrical system of aircraft is more complex to design and requires a new way to be conceived in order to reduce its weight. This paper describes how to optimize the weight of harnesses thanks to the Adaptive Multi-Agent System approach. This approach is based on the cooperation of agents that makes the global function of the system emerge. The communication between agents is the focus of this approach. We will develop this approach and apply it to the problem of the weight optimisation. The developed software provides results that are either equivalent or better than the ones with classical approaches. Moreover, the software may be a precious help to the engineer in charge of the design of harnesses as it enables to make different tests in a quasi-real time.
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Optimizing emergency medical assistance coordination in after-hours urgent surgery patients
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This paper treats the coordination of Emergency
Medical Assistance (EMA) and hospitals for afterhours
surgeries of urgent patients arriving to hospital by ambulance.
A standard hospital approach during night-shifts is to
have standby surgery teams come to hospital after alert to
cover urgent cases that cannot be covered by the in-house
surgery teams. In practice, the coordination process is manual and the
process management is case-specific, with great load on
human phone communication. In this paper, we propose
a multi-agent system based decision support system for automated and optimized coordination of hospitals, surgery teams on standby from home, and
ambulances to decrease the time to surgery of urgent
patients. The efficiency of the proposed model
is demonstrated over simulation experiments.
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Spatial Coordination Games for Large-Scale Visualization -
Direct Exchange Mechansims for Option Pricing
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This paper presents the design and simulation of direct exchange mechanisms for pricing European options. It extends McAfee's single-unit double auction to multi-unit format, and then applies it for pricing options through aggregating agent predictions of future asset prices. We will also propose the design of a combinatorial exchange for the simulation of agents using option trading strategies. We present several option trading strategies that are commonly used in real option markets to minimise the risk of future loss, and assume that agents can submit them as a combinatorial bid to the market maker. We provide simulation results for proposed mechanisms, and compare them with existing Black-Scholes mo
mostly used for option pricing. The simulation also tests the effect of supply and demand changes on option prices. It also takes into account agents with different implied volatility. We also observe how option prices are affected by the agents' choices of option trading strategies.
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Tableaux and Complexity Bounds for a Multiagent Justification Logic with Interacting Justifications
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The Logic of Proofs, LP, and its successor, Justification Logic, is a refinement of the modal logic approach to epistemology in which proofs/justifications are taken into account. In 2000 Kuznets showed that satisfiability for LP is in the second level of the polynomial hierarchy, a result which has been successfully repeated for all other one-agent justification logics whose complexity is known.
We introduce a family of multi-agent justification logics with interactions between the agents' justifications, by extending and generalizing the two-agent versions of LP introduced by Yavorskaya in 2008.
We present tableau rules and some complexity results. In several cases the satisfiability problem for these logics remains in the second level of the polynomial hierarchy, while the problem becomes PSPACE-hard for certain two-agent logics and there are EXP-hard logics of three agents. -
Arbitrary Announcements on Topological Subset Spaces
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Subset space semantics for public announcement logic in the spirit of
the effort modality have been proposed by Wang and Ågotnes [17] and by Bjorndahl
[6]. They propose to model the public announcement modality by shrinking
the epistemic range with respect to which a postcondition of the announcement
is evaluated, instead of by restricting the model to the set of worlds satisfying
the announcement. Thus we get an “elegant, model-internal mechanism for interpreting
public announcements” [6, p.12]. In this work, we extend Bjorndahl’s
logic PAL_int of public announcement, which is modelled on topological spaces
using subset space semantics and adding the interior operator, with an arbitrary
announcement modality, and we provide topological subset space semantics for
the corresponding arbitrary announcement logic APAL_int , and demonstrate completeness
of the logic by proving that it is equal in expressivity to the logic without
arbitrary announcements, employing techniques from [2, 13].
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STIT based deontic logics for the miners scenario
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In this paper we first develop two new STIT based deontic logics capable of solving the miners puzzle. The key idea is to use pessimistic lifting to lift the preference over worlds into the preference over sets of worlds.We also discuss a more general version of the miners puzzle in which plausibility is involved. In order to deal with the more general puzzle we add a modal operator representing plausibility to our logic. We present a sound and complete axiomatization.
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Conflict Resolution in Assumption-Based Frameworks (short)
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A number of non-monotonic logics have been considered for formal representation of agents' knowledge and their ability to draw conclusions. Assumption-Based Framework (ABF) is a well-established and general formalism which captures multiple existing non-monotonic logics based on default reasoning. In this paper, we show how also defeasible reasoning can be embedded into ABF. Differently from other similar proposals, we do not encode the conflict resolution mechanism for defeasible rules into the ABF's deductive systems. Instead, we formalize the notions of conflict and conflict resolution and make them part of the extended ABF framework. The user thus gains increased control over the entire conflict resolution process. Such an approach also allows to devise different domain-dependent conflict resolution strategies and to compare them. We also show, that no matter which conflict resolution strategy is used, our framework is able to guarantee certain desired properties.
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A dialogical model for collaborative decision making based on compromises (short)
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In this paper, we face the problem of group decision making
and propose a model of dialogue among a group of agents that have different
knowledge and preferences, but are willing to compromise in order
to collaboratively reach a common decision. Agents participating in such
a dialogue operate an internal reasoning to resolve conflicts emerging in
their knowledge during communication and reach a decision that requires
the least compromises. By enumerating a list of heuristics and strategies,
we achieve a targeted knowledge exchange, disclosing only the most relevant
information and achieving efficient decision-making, in terms of the
number of the exchanged messages.
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Modeling a Multi-issue Negotiation Protocol for Agent Extensible Negotiations (short)
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In this paper, we study how to achieve more effective negotiations by
extending during the negotiation process, the negotiation object with new relevant
items. Indeed, the possibility to extend the initial set of items defined by
the requester agent with other items related to the original query can help find
an agreement. In doing so, with extended proposals, the requester agent may
be incentive to be more flexible, e.g., by making concessions or relaxing some
constraints on the issues. This may help to achieve an agreement which is more
beneficial for both parties than breaking down the negotiation. Such extensible
negotiations may lead to win-win outcomes which otherwise can not be achieved
with some usual negotiation strategies where it is hard to dynamically alter the set
of items under negotiation during the course of the process. In this paper, we first
outline a negotiation strategy which allows the extension of the negotiation space
by extending the negotiation object with new relevant items. Based on this, we
then propose a new multi-issue negotiation protocol which relies on the bidding based
mechanism and deals with such extensible negotiation strategies. We show
that the proposed protocol guarantees several provable properties.
The Complexity of Strategic Argumentation under Grounded Semantics (short)
Thursday, December 18
| 8:00-8:50 | Registration |
| 8:50-9:00 | Opening |
| 9:00-10:00 | I.0: Keynote Talk: Agreement Computing |
| Prof. Carles Sierra (IIIA-CSIC, Spain) | |
| 10:00-10:30 | Coffee |
| 10:30-12:00 | I.1: Knowledge representation, Aggregation and Agreements |
| Patrick Krümpelmann, Gabriele Kern-Isberner and Tim Janus | |
| Henrique Viana and João Alcântara | |
| Arthur Carvalho, Stanko Dimitrov and Kate Larson | |
| Juan Carlos Nieves and Helena Lindgren | |
| 12:00-13:55 | Lunch |
| 13:55-15:10 | I.2: Coalitions, Cooperation, and Teamwork |
| Ted Scully and Michael Madden | |
| Changyun Wei, Koen Hindriks and Catholijn Jonker | |
| Mojtaba Malek Akhlagh and Jernej Polajnar | |
| Antonis Bikakis and Patrice Caire | |
| 15:10-15:40 | Coffee |
| 15:40-16:20 | I.3: Exploration, Interaction and Planning |
| Sebastian Ahrndt, Armin Aria and Johannes Fähndrich | |
| Jan Faigl, Olivier Simonin and Francois Charpillet | |
| Shervin Ghasemlou | |
| 16:20-16:30 | Short Break |
| 16:30-17:35 | I.4: Logic and Games |
| Maduka Attamah, Hans van Ditmarsch, Davide Grossi and Wiebe van der Hoek | |
| Orna Kupferman, Giuseppe Perelli and Moshe Vardi | |
| Piotr Kaźmierczak | |
| 19:30-23:00 | Social Dinner |
| (food from 8pm) |
Friday, December 19
| 9:00-10:00 | II.0: Keynote Talk: Verifiable Autonomy - (how) can you trust your robots? |
| Prof. Michael Fisher (University of Liverpool, UK) | |
| 10:00-10:30 | Coffee |
| 10:30-12:00 | II.1: Decision Making, Trust and Agent-Based Models |
| Nardine Osman, Alessandro Provetti, Valerio Riggi and Carles Sierra | |
| Franziska Klügl | |
| Reyhan Aydogan, Alexei Sharpanskykh and Julia Lo | |
| Guillem Francès, Xavier Rubio-Campillo, Carla Lancelotti and Marco Madella | |
| 12:00-13:30 | Lunch |
| 13:30-15:10 | II.2: Theories in Practice and Real-World Problems |
| Stéphanie Combettes, Thomas Sontheimer, Sylvain Rougemaille and Pierre Glize | |
| Marin Lujak, Holger Billhardt and Sascha Ossowski | |
| Andre Ribeiro and Eiko Yoneki | |
| Sarvar Abdullaev, Peter McBurney and Katarzyna Musial | |
| 15:10-15:40 | Coffee |
| 15:40-16:55 | II.3: Logic and Formal Approaches |
| Antonis Achilleos | |
| Hans van Ditmarsch, Sophia Knight and Aybüke Özgün | |
| Xin Sun and Zohreh Baniasadi | |
| 16:55-17:05 | Short Break |
| 17:05-17:45 | II.4: Argumentation and Negotiation |
| Martin Baláž, Jozef Frtús, Giorgos Flouris, Martin Homola and Ján Šefránek | |
| Dimitra Zografistou, Giorgos Flouris, Theodore Patkos, Dimitris Plexousakis, Martin Baláž, Martin Homola and Alexander Simko | |
| Samir Aknine, Souhila Arib and Boukredera Djamila | |
| Guido Governatori, Michael Maher, Francesco Olivieri, Antonino Rotolo and Simone Scannapieco | |
| 17:45-18:00 | Clossing session |
Keynote Speakers
Professor Michael Fisher (University of Liverpool)
Department of Computer Science, University of Liverpool, UK http://intranet.csc.liv.ac.uk/~michael
Talk: Verifiable Autonomy - (how) can you trust your robots?
As the use of autonomous systems and robotics spreads, the need for their activities to not only be understandable and explainable, but even verifiable, is increasing. But how can we be sure what such a system will decide to do, and can we really formally verify this behaviour?
Practical autonomous systems are increasingly based on some form of hybrid agent architecture, at the heart of which is an agent that makes many, and possibly all, of the decisions that the human operator used to make. However it is important that these agents are "rational", in the sense that they not only make decisions, but have explicit and explainable reasons for making those decisions.
In this talk, I will examine these "rational" agents, discuss their role at the heart of autonomous systems, and explain how we can formally verify their behaviours. This then allows us: to be more confident about what our autonomous systems will decide to do; to use formal arguments in system certification and safety; and even to analyse ethical decisions our systems might make.
Biography
Michael Fisher is Professor of Computer Science and Director of the multi-disciplinary Centre for Autonomous Systems Technology at the University of Liverpool. His research particularly concerns practical temporal proof, agents, formal verification, and autonomous systems, with recent work including automated verification systems for agent programs, hybrid agent architectures for autonomous satellites, formal verification for use in the certification of autonomous unmanned air systems, and the formal verification of swarm robotics.
He serves on the editorial boards of both the Journal of Applied Logic and the Annals of Mathematics and Artificial Intelligence, and is a corner editor for the Journal of Logic and Computation. He is a Fellow of both the BCS and the IET, and currently leads UK research projects on "Reconfigurable Autonomy", "Trustworthy Robotic Assistants", and "Verifiable Autonomy".
Professor Carles Sierra (IIIA-CSIC)
IIIA-CSIC, Spain http://www.iiia.csic.es/~sierra/public/Home.html
Talk: Agreement Computing
In modern IT-enabled societies, the human user is being assisted with an increasing number of tasks by computational communicating entities/software (usually called agents). Agents interact with and act on behalf of their human users. Their assistance could take different forms, starting with simple technical support such as email filtering, information retrieval, shopping, etc., and moving towards full delegation of more complex tasks, such as service composition for travel organization, dispute resolution in the context of divorces, labour controversies, traffic accidents, etc.
To support the agents with the more complex tasks, we argue that the concept of “agreement” lies at the basis of agent communication and interaction. Interacting agents will need to base their decisions and actions on explicit agreements. Agreement Computing aims at proposing a plethora of adequate theoretical methods and applied techniques in order to allow for the design and implementation of those new generation “intelligent" communicating artefacts that will form the basis of future modern "mixed" societies populated by interconnected and mutually interacting humans and artefacts.
Biography
Carles Sierra is currently vice-director of the Artificial Intelligence Research Institute with around 80 members and has been Head of the Intelligent Systems Department for five years. He has led around twenty projects to a successful end. He has been the IIIA PI in several previous EU projects, manager of the EUTIST-AMI Cluster, PI of a recent very large Spanish research project that has involved around 100 researchers (Agreement Technologies), and has participated as researcher in many others. He has served as project evaluator in different IST calls during FP V, VI and VII and has been appointed as reviewer of twenty EU R&D projects. He is also regular evaluator of the NSF in the USA, and of the Netherlands, Spanish, Irish, British and Argentinian Research Funding Agencies.
He has over 300 publications in his areas of research. He was a member of the management board of the AgentLink network of excellence (and its subsequent AgentLink II) and the co-ordinator of its special interest group on Agent-mediated Electronic Commerce with around a hundred members all over Europe. He has organised several workshops in the area of agents and electronic commerce. He is member of the program committees of around a dozen conferences and workshops per year, and is a member of seven journal editorial boards including some of the most prestigious journals on Artificial Intelligence: AIJ and JAIR. He is one of the editors in chief of JAAMAS. He was General Chair of the conference Autonomous Agents 2000, AAMAS 2009, and PC chair of the AAMAS 2004. He is also a PC chair of IJCAI 2017. He was the local chair for IJCAI 2011. He has been the President of the Catalan Association of Artificial Intelligence (1998- 2002).
List of Accepted Papers
LIST OF ACCEPTED FULL PAPERS
- Sebastian Ahrndt, Armin Aria and Johannes Fähndrich. Ants in the OCEAN: Modulating Agents with Personality for Planning with Humans
- Antonis Bikakis and Patrice Caire. Computing coalitions in Multiagent Systems, A contextual reasoning approach
- Arthur Carvalho, Stanko Dimitrov and Kate Larson. A Study on the Influence of the Number of MTurkers on the Quality of the Aggregate Output
- Reyhan Aydogan, Alexei Sharpanskykh and Julia Lo. A Trust-based Situation Awareness Model
- Changyun Wei, Koen Hindriks and Catholijn Jonker. Auction-Based Dynamic Task Allocation for Foraging with a Cooperative Robot Team
- Xin Sun and Zohreh Baniasadi. STIT based deontic logics for the miners scenario
- Henrique Viana and João Alcântara. Priority-based Merging Operator without Distance Measures
- Stéphanie Combettes, Thomas Sontheimer, Sylvain Rougemaille and Pierre Glize. The multi-agent cooperation for optimizing the weight of electrical aircraft harnesses
- Orna Kupferman, Giuseppe Perelli and Moshe Vardi. Synthesis with Rational Environments
- Patrick Krümpelmann, Gabriele Kern-Isberner and Tim Janus. Angerona - A flexible Multiagent Framework for Knowledge-based Agents
- Andre Ribeiro and Eiko Yoneki. Spatial Coordination Games for Large-Scale Visualization
- Sarvar Abdullaev, Peter McBurney and Katarzyna Musial. Direct Exchange Mechansims for Option Pricing
- Marin Lujak, Holger Billhardt and Sascha Ossowski. Optimizing emergency medical assistance coordination in after-hours urgent surgery patients
- Ted Scully and Michael Madden. Facilitating Multi-Agent Coalition Formation through Cooperation in Self-Interested Environments
- Maduka Attamah, Hans van Ditmarsch, Davide Grossi and Wiebe van der Hoek. A Framework for Epistemic Gossip Protocols
- Hans van Ditmarsch, Sophia Knight and Aybüke Özgün. Arbitrary Announcements on Topological Subset Spaces
- Shervin Ghasemlou. Homecoming: A multi-robot exploration method for conjunct environments with a systematic return procedure
- Antonis Achilleos. Tableaux and Complexity Bounds for a Multiagent Justification Logic with Interacting Justifications
- Franziska Klügl. Affordance-Based Interaction Design for Agent-Based Simulation Models
- Mojtaba Malek Akhlagh and Jernej Polajnar. Distributed Deliberation on Direct Help in Agent Teamwork
- Nardine Osman, Alessandro Provetti, Valerio Riggi and Carles Sierra. MORE: Merged Opinions Reputation Model
LIST OF ACCEPTED SHORT PAPERS
- Martin Baláž, Jozef Frtús, Giorgos Flouris, Martin Homola and Ján Šefránek. Conflict Resolution in Assumption-Based Frameworks
- Piotr Kaźmierczak. Compliance Games
- Guido Governatori, Michael Maher, Francesco Olivieri, Antonino Rotolo and Simone Scannapieco. The Complexity of Strategic Argumentation under Grounded Semantics
- Dimitra Zografistou, Giorgos Flouris, Theodore Patkos, Dimitris Plexousakis, Martin Baláž, Martin Homola and Alexander Simko. A dialogical model for collaborative decision making based on compromises
- Jan Faigl, Olivier Simonin and Francois Charpillet. Comparison of Task-Allocation Algorithms in Frontier-Based Multi-Robot Exploration
- Guillem Francès, Xavier Rubio-Campillo, Carla Lancelotti and Marco Madella. Decision Making in Agent-Based Models
- Samir Aknine, Souhila Arib and Boukredera Djamila. Modeling a Multi-issue Negotiation Protocol for Agent Extensible Negotiations
- Juan Carlos Nieves and Helena Lindgren. Deliberative Argumentation for Service Provision in Smart Environments