Showing papers presented at "European Workshop on Multi-Agent Systems in 2014"

Arbitrary Announcements on Topological Subset Spaces

Abstract: Subset space semantics for public announcement logic in the spirit of the effort modality have been proposed by Wang and Agotnes [18] 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].

Comparison of Task-Allocation Algorithms in Frontier-Based Multi-robot Exploration

Abstract: In this paper, we address the problem of efficient allocation of the navigational goals in the multi-robot exploration of unknown environment. Goal candidate locations are repeatedly determined during the exploration. Then, the assignment of the candidates to the robots is solved as the task-allocation problem. A more frequent decision-making may improve performance of the exploration, but in a practical deployment of the exploration strategies, the frequency depends on the computational complexity of the task-allocation algorithm and available computational resources. Therefore, we propose an evaluation framework to study exploration strategies independently on the available computational resources and we report a comparison of the selected task-allocation algorithms deployed in multi-robot exploration.

Synthesis with Rational Environments

Abstract: 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.

A Framework for Epistemic Gossip Protocols

Abstract: We implement a framework to evaluate epistemic gossip protocols. Gossip protocols spread information within a network of agents by pairwise communications. This tool, Epistemic Gossip Protocol (EGP), is applied to epistemic gossip protocols presented in [1]. We introduce a programming language for epistemic gossip protocols. We describe an interpreter for this language, together with a model generator and model checker, for a dynamic model of the protocol. The tool EGP outputs key dynamic properties of such protocols, thus facilitating the process of protocol design and planning. We conclude with some experimental results.

Deliberative Argumentation for Service Provision in Smart Environments

Abstract: 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

Ants in the OCEAN: Modulating Agents with Personality for Planning with Humans

Abstract: 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. Furthermore, it is outlined how these variations can be used for the planning process in collaborative settings.

Decision Making in Agent-Based Models

Abstract: 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.

A Trust-Based Situation Awareness Model

Abstract: 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.

Affordance-Based Interaction Design for Agent-Based Simulation Models

Abstract: 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.

Tableaux and Complexity Bounds for a Multiagent Justification Logic with Interacting Justifications

Abstract: We introduce a family of multi-agent justification logics with interactions between the agents’ justifications, by extending and generalizing the two-agent versions of the Logic of Proofs (LP) introduced by Yavorskaya in 2008. LP, and its successor, Justification Logic, is a refinement of the modal logic approach to epistemology in which for every belief assertion, an explicit justification is supplied. This affects the complexity of the logic’s derivability problem, which is known to be in the second level of the polynomial hierarchy (first result by Kuznets in 2000) for all single-agent justification logics whose complexity is known. 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.

Optimizing Emergency Medical Assistance Coordination in After-Hours Urgent Surgery Patients

Abstract: This paper treats the coordination of Emergency Medical Assistance (EMA) and hospitals for after-hours surgeries of urgent patients arriving 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. This approach results in a considerable decrease in staffing costs in respect to having sufficient permanent in-house staff. Therefore, coordinating EMA and the hospitals in a region with their outhouse staff with the objective to have as fast urgent surgery treatments as possible with minimized cost is a crucial parameter of the medical system efficiency and as such deserves a thorough investigation. In practice, the process is manual and the process management is case-specific, with great load on human phone communication. In this paper, we propose a decision support system for the automated 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 proven over simulation experiments.

Computing Coalitions in Multiagent Systems: A Contextual Reasoning Approach

Abstract: In multiagent systems, agents often have to rely on other agents to reach their goals, for example when they lack a needed resource or do not have the capability to perform a required action. Agents therefore need to cooperate. Some of the questions then raised, such as, which agent to cooperate with, are addressed in the field of coalition formation. In this paper we go further and first, address the question of how to compute the solution space for the formation of coalitions using a contextual reasoning approach. We model agents as contexts in Multi-Context Systems (MCS) and dependence relations among agents as bridge rules. We then systematically compute all potential coalitions using algorithms for MCS equilibria. Finally, given a set of functional and non-functional requirements, we propose ways to select the best solutions. We illustrate our approach with an example from robotics.

A Study on the Influence of the Number of MTurkers on the Quality of the Aggregate Output

Abstract: 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.

Homecoming: A Multi-robot Exploration Method for Conjunct Environments with a Systematic Return Procedure

Abstract: The present work proposes a multi-robot exploration method for conjunct environments, based on one of the state-of-the-art algorithms. In many exploration missions, after the subject is found, it is beneficial if the discoverer robot returns back to the base station, in order to report, delivery or recharge. In addition, the exploration might need a long time to be finished or has to be done over and over. Returning back to the base station enables robots to get recharged, fixed, or even substituted with other robots. Furthermore, the equilibrium in task allocation to robots is this work’s other concern. The presented algorithm also reduces the maximum energy consumption of robots, as a good side effect. The efficiency of the proposed algorithm is demonstrated by providing simulation results for a variety of obstacle densities and different number of robots.

Conflict Resolution in Assumption-Based Frameworks

Abstract: We show how defeasible reasoning can be embedded into ABF Differently from other 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 (XABF) This improves the control over the conflict resolution process, and allows to devise and compare different domain-dependent conflict resolution strategies We also show, that no matter which conflict resolution strategy is used, our framework is able to guarantee certain desired properties

Auction-Based Dynamic Task Allocation for Foraging with a Cooperative Robot Team

Abstract: 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 and executed 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.

Forming Coalitions in Self-interested Multi-agent Environments Through the Promotion of Fair and Cooperative Behaviour

Abstract: 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. The problem we consider is how to facilitate the formation of a coalition in a competitive marketplace, 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 different 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 here. ACCORD manages real-world complexities by promoting the adoption of cooperative behaviour amongst agents. Through extensive empirical analysis we analyse the ACCORD protocol and demonstrate that cooperative and fair behaviour is dominant and any agents deviating from this behaviour perform less well over time.

STIT Based Deontic Logics for the Miners Puzzle

Abstract: 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. Then 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. Lastly we present a sound and complete axiomatization.

Priority-based Merging Operator without Distance Measures ?

Abstract: 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.

Angerona - A Flexible Multiagent Framework for Knowledge-Based Agents

Abstract: 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. As the basis for this framework we propose and formalize a general concept of compound agents in which we consider agents to consist of hierarchies of interacting epistemic and functional components. Each epistemic component is instantiated by a knowledge representation formalism. 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 instantiations define sub-frameworks for, e. g., the development of BDI agents and variants thereof. The Angerona framework realizes this concept by means of a flexible JAVA plug-in architecture for the epistemic and the functional components of an agent. The epistemic plug-ins are based on the Tweety library for knowledge representation, which provides various ready-for-use implementations and knowledge representation formalisms and a framework for the implementation of additional ones. Angerona already contains several partial and complete instantiations that implement several approaches. Angerona also features an environment plug-in for communicating agents and a flexible GUI to monitor the multiagent system and the inner workings of the agents, particularly the inspection of the dynamics of their epistemic states. Angerona and Tweety are ready to use, well documented, and open source.

MORE: Merged Opinions Reputation Model

Abstract: 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 modeling 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 behavioral 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 behavior of football teams in football matches, which we argue is a very interesting yet difficult approach for evaluating the model.

A Dialogical Model for Collaborative Decision Making Based on Compromises

Abstract: In this paper, we deal with group decision making and propose a model of dialogue among agents that have different knowledge and preferences, but are willing to compromise in order to collaboratively reach a common decision. Agents participating in the dialogue use internal reasoning to resolve conflicts emerging in their knowledge during communication and to reach a decision that requires the least compromises. Our approach has significant potential, as it may allow targeted knowledge exchange, partial disclosure of information and efficient or informed decision-making depending on the topic of the agents’ discussion.

Strategic argumentation under grounded semantics is NP-complete

Abstract: We study the complexity of the Strategic Argumentation Problem for 2-player dialogue games where a player should decide what move to play at each turn in order to prove (disprove) a given claim. We shall prove that this is an NP-complete problem. The result covers one the most popular argumentation semantics proposed by Dung [4]: the grounded semantics.

Modeling a Multi-issue Negotiation Protocol for Agent Extensible Negotiations

Abstract: 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 incentivized 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.

Distributed Deliberation on Direct Help in Agent Teamwork

Abstract: 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.

Multi-Agent Cooperation for Optimizing Weight of Electrical Aircraft Harnesses

Abstract: This paper deals with minimizing aircraft electrical system weight. Because of technological advances that are spreading, electrical system of aircraft is more complex to design and requires new way to be conceived in order to reduce its weight. This paper describes how to optimize weight of harnesses thanks to the Adaptive Multi-Agent System approach. This approach is based on agent cooperation which makes global function of system emerge. Communication between agents is the focus of this approach. We will develop this approach and apply it to the weight optimisation problem. The developed software provides results that are either equivalent or better than those of classical approaches. Moreover, this software may be a precious help to engineer in charge of designing harnesses as it enables to make different tests in a quasi-real time.

Spatial Coordination Games for Large-Scale Visualization

Abstract: Dimensionality reduction (‘visualization’) is a central problem in statistics. Several of the most popular solutions grew out of interaction metaphors (springs, boids, neurons, etc.) We show that the problem can be framed as a game of coordination and solved with standard game-theoretic concepts. Nodes that are close in a (high-dimensional) graph must coordinate in a (low-dimensional) screen position. We derive a game solution, a GPU-parallel implementation and report visualization experiments in several datasets. The solution is a very practical application of game-theory in an important problem, with fast and low-stress embeddings.

Direct Exchange Mechanisms for Option Pricing

Abstract: 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 model 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.