Analysis of emergent symmetry breaking in collective decision making

We investigate a simulated multi-agent system (MAS) that collectively decides to aggregate at an area of high utility. The agents’ control algorithm is based on random agent–agent encounters and is inspired by the aggregation behavior of honeybees. In this article, we define symmetry breaking, several symmetry breaking measures, and report the phenomenon of emergent symmetry breaking within our observed system. The ability of the MAS to successfully break the symmetry depends significantly on a local-neighborhood-based threshold of the agents’ control algorithm that determines at which number of neighbors the agents stop. This dependency is analyzed and two macroscopic features are determined that significantly influence the symmetry breaking behavior. In addition, we investigate the connection between the ability of the MAS to break symmetries and the ability to stay flexible in a dynamic environment.     

Learning to trust in the competence and commitment of agents

For agents to collaborate in open multi-agent systems, each agent must trust in the other agents’ ability to complete tasks and willingness to cooperate. Agents need to decide between cooperative and opportunistic behavior based on their assessment of another agents’ trustworthiness. In particular, an agent can have two beliefs about a potential partner that tend to indicate trustworthiness: that the partner is competent and that the partner expects to engage in future interactions. This paper explores an approach that models competence as an agent’s probability of successfully performing an action, and models belief in future interactions as a discount factor. We evaluate the underlying decision framework’s performance given accurate knowledge of the model’s parameters in an evolutionary game setting. We then introduce a game-theoretic framework in which an agent can learn a model of another agent online, using the Harsanyi transformation. The learning agents evaluate a set of competing hypotheses about another agent during the simulated play of an indefinitely repeated game. The Harsanyi strategy is shown to demonstrate robust and successful online play against a variety of static, classic, and learning strategies in a variable-payoff Iterated Prisoner’s Dilemma setting.     

Preventive maintenance scheduling for repairable system with deterioration

Maintenance as an important part in manufacturing system can keep equipment in good condition. Many maintenance policies help to decrease the unexpected failures and reduce high operational cost such as conventional preventive maintenance. But these conventional preventive maintenance policies have the same time interval T that may easily neglect system’s reliability, because the system deteriorates with increased usage and age. Hence, this study has developed a reliability-centred sequential preventive maintenance model for monitored repairable deteriorating system. It is supposed that system’s reliability could be monitored continuously and perfectly, whenever it reaches the threshold R, the imperfect repair must be performed to restore the system. In this model, system’s failure rate function and operational cost are both considered by the effect of system’s corresponding condition, which helps to decide the optimal reliability threshold R and preventive maintenance cycle number. Finally, through case study, the simulation results show that the improved sequential preventive maintenance policy is more practical and efficient.     

A game-theoretic framework to identify overlapping communities in social networks

In this paper, we introduce a game-theoretic framework to address the community detection problem based on the structures of social networks. We formulate the dynamics of community formation as a strategic game called community formation game: Given an underlying social graph, we assume that each node is a selfish agent who selects communities to join or leave based on her own utility measurement. A community structure can be interpreted as an equilibrium of this game. We formulate the agents’ utility by the combination of a gain function and a loss function. We allow each agent to select multiple communities, which naturally captures the concept of “overlapping communities”. We propose a gain function based on the modularity concept introduced by Newman (Proc Natl Acad Sci 103(23):8577–8582, 2006), and a simple loss function that reflects the intrinsic costs incurred when people join the communities. We conduct extensive experiments under this framework, and our results show that our algorithm is effective in identifying overlapping communities, and are often better then other algorithms we evaluated especially when many people belong to multiple communities. To the best of our knowledge, this is the first time the community detection problem is addressed by a game-theoretic framework that considers community formation as the result of individual agents’ rational behaviors.     

A concrete realization of the Hoare powerdomain

The lattice of non-empty Scott-closed subsets of a domain D is called the Hoare powerdomain of D. The Hoare powerdomain is used in programming semantics as a model for angelic nondeterminism. In this paper, we show that the Hoare powerdomain of any domain can be realized as the lattice of full subinformation systems of the domain’s corresponding information system as well as the lattice of non-empty down-sets of the system’s consistency predicate.     

Is There a Future for AI Without Representation?

This paper investigates the prospects of Rodney Brooks’ proposal for AI without representation. It turns out that the supposedly characteristic features of “new AI” (embodiment, situatedness, absence of reasoning, and absence of representation) are all present in conventional systems: “New AI” is just like old AI. Brooks proposal boils down to the architectural rejection of central control in intelligent agents—Which, however, turns out to be crucial. Some of more recent cognitive science suggests that we might do well to dispose of the image of intelligent agents as central representation processors. If this paradigm shift is achieved, Brooks’ proposal for cognition without representation appears promising for full-blown intelligent agents—Though not for conscious agents.     

Evolutionary mechanism design: a review

The advent of large-scale distributed systems poses unique engineering challenges. In open systems such as the internet it is not possible to prescribe the behaviour of all of the components of the system in advance. Rather, we attempt to design infrastructure, such as network protocols, in such a way that the overall system is robust despite the fact that numerous arbitrary, non-certified, third-party components can connect to our system. Economists have long understood this issue, since it is analogous to the design of the rules governing auctions and other marketplaces, in which we attempt to achieve socially-desirable outcomes despite the impossibility of prescribing the exact behaviour of the market participants, who may attempt to subvert the market for their own personal gain. This field is known as “mechanism design”: the science of designing rules of a game to achieve a specific outcome, even though each participant may be self-interested. Although it originated in economics, mechanism design has become an important foundation of multi-agent systems (MAS) research. In a traditional mechanism design problem, analytical methods are used to prove that agents’ game-theoretically optimal strategies lead to socially desirable outcomes. In many scenarios, traditional mechanism design and auction theory yield clear-cut results; however, there are many situations in which the underlying assumptions of the theory are violated due to the messiness of the real-world. In this paper we review alternative approaches to mechanism design which treat it as an engineering problem and bring to bear engineering design principles, viz.: iterative step-wise refinement of solutions, and satisficing instead of optimization in the face of intractable complexity. We categorize these approaches under the banner of evolutionary mechanism design.     

Adaptation and user expertise modelling in AthosMail

This article describes the User Model component of AthosMail, a speech-based interactive e-mail application developed in the context of the EU project DUMAS. The focus is on the system’s adaptive capabilities and user expertise modelling, exemplified through the User Model parameters dealing with initiative and explicitness of the system responses. The purpose of the conducted research was to investigate how the users could interact with a system in a more natural way, and the two aspects that mainly influence the system’s interaction capabilities, and thus the naturalness of the dialogue as a whole, are considered to be the dialogue control and the amount of information provided to the user. The User Model produces recommendations of the system’s appropriate reaction depending on the user’s observed competence level, monitored and computed on the basis of the user’s interaction with the system. The article also discusses methods for the evaluation of adaptive user models and presents results from the AthosMail evaluation.The research was done while the author was affiliated with the University of Art and Design Helsinki as the scientific coordinator of the DUMAS project.     

Artificial institutions: a model of institutional reality for open multiagent systems

Software agents’ ability to interact within different open systems, designed by different groups, presupposes an agreement on an unambiguous definition of a set of concepts, used to describe the context of the interaction and the communication language the agents can use. Agents’ interactions ought to allow for reliable expectations on the possible evolution of the system; however, in open systems interacting agents may not conform to predefined specifications. A possible solution is to define interaction environments including a normative component, with suitable rules to regulate the behaviour of agents. To tackle this problem we propose an application-independent metamodel of artificial institutions that can be used to define open multiagent systems. In our view an artificial institution is made up by an ontology that models the social context of the interaction, a set of authorizations to act on the institutional context, a set of linguistic conventions for the performance of institutional actions and a system of norms that are necessary to constrain the agents’ actions.     

An interleaving crescent broadcasting protocol for near video-on-demand services

This work presents a novel interleaving crescent broadcasting protocol for near video-on-demand service. The interleaving crescent broadcasting protocol is a trade-off among the subscriber’s access latency, maximum buffer requirement, needed subscriber’s bandwidth, and maximum disk I/O transfer rate. A longer subscriber’s access latency may cause a subscriber to leave. A lower maximum buffer requirement, a lower needed subscriber’s bandwidth, and a lower maximum disk I/O transfer rate reduce subscribers’ costs. The interleaving crescent broadcasting protocol not only makes access latency shorter, but also lowers the overall system’s cost. We prove the correctness of the interleaving crescent protocol; provide mathematical analyses to demonstrate its efficiency.     

Proposal of a recommender system simulator based on a small-world model

This article proposes the development of a software simulator that allows the user to evaluate algorithms for recommender systems. This simulator consists of agents, items, a recommender, a controller, and a recorder, and it locates the agents and allocates the items based on a small-world network. An agent plays the role of a user in the recommender system, and the recommender also plays a role in the system. The controller handles the simulation flow where (1) the recommender recommends items to agents based on the recommendation algorithm, (2) each agent evaluates the items based on the agents’ rating algorithm and using the attributes of each item and agent, and (3) the recorder obtains the results of the rating and evaluation measurements for the recommendation pertaining to such information as precision and recall. This article considers the background of the proposal and the architecture of the simulator.     

Epistemic formulae,argument structures,and a narrative on identity and deception: a formal representation from the AJIT subproject within AURANGZEB

The modelling of deception is an active field in artificial intelligence. Episodic formulae were introduced, applied and refined in several previous articles, some of them concerned with identity, and some, with tasks of guessing agents’ minds. The AURANGZEB project is specifically concerned with combining this kind of representation with argument structure data (in particular, Toulmin’s). Two previous articles were concerned with the MURAD subproject, whereas this paper introduces the AJIT model. In MURAD, the kind of deception consisted of having a victim misunderstand a perpetrator’s real intentions. In AJIT, instead, a powerful agent is outsmarted by means of exchanged identities.      

Tests for consistent measurement of external subjective software quality attributes

One reason that researchers may wish to demonstrate that an external software quality attribute can be measured consistently is so that they can validate a prediction system for the attribute. However, attempts at validating prediction systems for external subjective quality attributes have tended to rely on experts indicating that the values provided by the prediction systems informally agree with the experts’ intuition about the attribute. These attempts are undertaken without a pre-defined scale on which it is known that the attribute can be measured consistently. Consequently, a valid unbiased estimate of the predictive capability of the prediction system cannot be given because the experts’ measurement process is not independent of the prediction system’s values. Usually, no justification is given for not checking to see if the experts can measure the attribute consistently. It seems to be assumed that: subjective measurement isn’t proper measurement or subjective measurement cannot be quantified or no one knows the true values of the attributes anyway and they cannot be estimated. However, even though the classification of software systems’ or software artefacts’ quality attributes is subjective, it is possible to quantify experts’ measurements in terms of conditional probabilities. It is then possible, using a statistical approach, to assess formally whether the experts’ measurements can be considered consistent. If the measurements are consistent, it is also possible to identify estimates of the true values, which are independent of the prediction system. These values can then be used to assess the predictive capability of the prediction system. In this paper we use Bayesian inference, Markov chain Monte Carlo simulation and missing data imputation to develop statistical tests for consistent measurement of subjective ordinal scale attributes.     

Stealth modification versus nuisance attacks in the LOCKSS peer-to-peer digital preservation system

The LOCKSS system is a world-wide peer-to-peer system for the preservation of academic journals and other archival information published on the Web. The system is deployed at over 200 libraries around the world and currently preserving titles of publishers representing more than 2000 academic titles. It consists of a large number of independent, low-cost, persistent Web caches that cooperate to detect and repair damage to their content by voting in “opinion polls.” Based on our experience working with the LOCKSS system, in this paper, we identify and characterize a fundamental tradeoff in the system’s ability to defend against adversaries with competing goals. In particular, the techniques used to defend against adversaries attempting stealth modification of content being preserved in the system are at odds with those used to identify nuisance adversaries simply trying to disrupt system procedures. We show that with a simple change to the design of the system’s preservation protocol we are able to create moderately strong defense against both of these competing adversaries simultaneously.     

Automata for Epistemic Temporal Logic with Synchronous Communication

We suggest that developing automata theoretic foundations is relevant for knowledge theory, so that we study not only what is known by agents, but also the mechanisms by which such knowledge is arrived at. We define a class of epistemic automata, in which agents’ local states are annotated with abstract knowledge assertions about others. These are finite state agents who communicate synchronously with each other and information exchange is ‘perfect’. We show that the class of recognizable languages has good closure properties, leading to a Kleene-type theorem using what we call regular knowledge expressions. These automata model distributed causal knowledge in the following way: each agent in the system has a partial knowledge of the temporal evolution of the system, and every time agents synchronize, they update each other’s knowledge, resulting in a more up-to-date view of the system state. Hence we show that these automata can be used to solve the satisfiability problem for a natural epistemic temporal logic for local properties. Finally, we characterize the class of languages recognized by epistemic automata as the regular consistent languages studied in concurrency theory.     

Assessing architectural evolution: a case study

This paper proposes to use a historical perspective on generic laws, principles, and guidelines, like Lehman’s software evolution laws and Martin’s design principles, in order to achieve a multi-faceted process and structural assessment of a system’s architectural evolution. We present a simple structural model with associated historical metrics and visualizations that could form part of an architect’s dashboard. We perform such an assessment for the Eclipse SDK, as a case study of a large, complex, and long-lived system for which sustained effective architectural evolution is paramount. The twofold aim of checking generic principles on a well-know system is, on the one hand, to see whether there are certain lessons that could be learned for best practice of architectural evolution, and on the other hand to get more insights about the applicability of such principles. We find that while the Eclipse SDK does follow several of the laws and principles, there are some deviations, and we discuss areas of architectural improvement and limitations of the assessment approach.     

Studying high reliability systems in the probabilistic school of B. V. Gnedenko

We give a survey of the works of B.V. Gnedenko’s reliability school, starting from 1950s and up until the latest years in two directions: (1) invariance of state distributions for queueing systems and networks, (2) asymptotic behavior of a redundant system’s characteristics under low load.     

Selection of modeling coordinates for forward dynamic multibody simulations

Modeling mechanical systems in a manner that allows the models to be simulated quickly is vital in many fields, such as real-time simulation and control. Modeling these systems using their symbolic equations, rather than the more widely-used numerical methods, generally produces faster solution times. However, the number, complexity, and computational efficiency of these equations is highly dependent upon which coordinate set was used to model the system. Most coordinate selection methods established thus far are based on the assumption that minimizing the number of modeling coordinates will produce models with faster simulation times. This paper will show that this technique is not always valid and proposes a new technique of selecting a system’s coordinates based on a series of heuristics. A large part of these heuristics will be established by closely analyzing a specific technique used to formulate a system’s equations, and the effect each step of this formulation process will have on the complexity of the final system equations.     

Handling Communication Restrictions and Team Formation in Congestion Games

There are many domains in which a multi-agent system needs to maximize a “system utility” function which rates the performance of the entire system, while subject to communication restrictions among the agents. Such communication restrictions make it difficult for agents that take actions to optimize their own “private” utilities to also help optimize the system utility. In this article we show how previously introduced utilities that promote coordination among agents can be modified to be effective in domains with communication restrictions. The modified utilities provide performance improvements of up to 75 over previously used utilities in congestion games (i.e., games where the system utility depends solely on the number of agents choosing a particular action). In addition, we show that in the presence of severe communication restrictions, team formation for the purpose of information sharing among agents leads to an additional 25 improvement in system utility. Finally, we show that agents’ private utilities and team sizes can be manipulated to form the best compromise between how “aligned” an agent’s utility is with the system utility and how easily an agent can learn that utility.