Agent communication and artificial institutions

In this paper we propose an application-independent model for the definition of artificial institutions that can be used to define open multi-agent systems. Such a model of institutional reality makes us able also to define an objective and external semantics of a commitment-based Agent Communication Language (ACL). In particular we propose to regard an ACL as a set of conventions to act on a fragment of institutional reality, defined in the context of an artificial institution. Another contribution of the work presented in this paper is an operational definition of norms, a crucial component of artificial institutions. In fact in open systems interacting agents might not conform to the specifications. We regard norms as event-driven rules that when are fired by events happening in the system create or cancel a set of commitments. An interesting aspect of our proposal is that both the definition of the ACL and the definition of norms are based on the same notion of commitment. Therefore an agent capable of reasoning on commitments can reason on the semantics of communicative acts and on the system of norms.     

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.     

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.     

Normative Communication Models for Agent

An agent message is an attempted action upon the information state of the receiver that, if successful, would cause the receiver to move to a new information state. A model of normative communication can define when messages are not merely unsuccessful but instead are illegal or impossible actions upon the receiver’s internal state. The model uses the preconditions of the other core message types, coupled with a model of task interdependencies, agent roles, and belief-desire-intention elements, to define the preconditions for sending a canonical not-understood error message. By defining the space of messages that are legal actions on an agent’s internal state, a normative communication model also defines a set of ‘reasons’ that can accompany the error message. A not-understood error message signals a mismatch between agent interaction models and the accompanying reason opens the possibility for agents to realign their respective models. The paper discusses the matters arising from this possibility. This approach assumes that normative communication behavior reflects normative domain behavior. It also assumes that each agent accesses the normative model, in contrast with more centralized frameworks for defining normative interaction among agents and identifying interaction errors.     

Semantic technologies for open interaction systems

Open interaction systems play a crucial role in agreement technologies because they are software devised for enabling autonomous agents (software or human) to interact, negotiate, collaborate, and coordinate their activities in order to establish agreements and manage their execution. Following the approach proposed by the recent literature on agent environments those open distributed systems can be efficiently and effectively modeled as a set of correlated physical and institutional spaces of interaction where objects and agents are situated. In our view in distributed open systems, spaces are fundamental for modeling the fact that events, actions, and social concepts (like norms and institutional objects) should be perceivable only by the agents situated in the spaces where they happen or where they are situated. Institutional spaces are also crucial for their active functional role of keeping track of the state of the interaction, and for monitoring and enforcing norms. Given that in an open distributed and dynamic system it is fundamental to be able to create and destroy spaces of interaction at run-time, in this paper we propose to create them using Artificial Institutions (AIs) specified at design time. This dynamic creation is a complex task that deserves to be studied in all details. For doing that, in this paper, we will first define the various components of AIs and spaces using Semantic Web Technologies, then we will describe the mechanisms for using AIs specification for realizing spaces of interaction. We will exemplify this process by formalizing the components of the auction Artificial Institution and of the spaces created for running concrete auctions.     

An inquiry dialogue system

The majority of existing work on agent dialogues considers negotiation, persuasion or deliberation dialogues; we focus on inquiry dialogues, which allow agents to collaborate in order to find new knowledge. We present a general framework for representing dialogues and give the details necessary to generate two subtypes of inquiry dialogue that we define: argument inquiry dialogues allow two agents to share knowledge to jointly construct arguments; warrant inquiry dialogues allow two agents to share knowledge to jointly construct dialectical trees (essentially a tree with an argument at each node in which a child node is a counter argument to its parent). Existing inquiry dialogue systems only model dialogues, meaning they provide a protocol which dictates what the possible legal next moves are but not which of these moves to make. Our system not only includes a dialogue-game style protocol for each subtype of inquiry dialogue that we present, but also a strategy that selects exactly one of the legal moves to make. We propose a benchmark against which we compare our dialogues, being the arguments that can be constructed from the union of the agents’ beliefs, and use this to define soundness and completeness properties that we show hold for all inquiry dialogues generated by our system.     

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.     

An abstract knowledge based approach to diagnosis and recovery of plan failure in multi-agent systems

Open multi-agent systems (MAS) are decentralised and distributed systems that consist of a large number of loosely coupled autonomous agents. In the absence of centralised control they tend to be difficult to manage, especially in an open environment, which is dynamic, complex, distributed and unpredictable. This dynamism and uncertainty in an open environment gives rise to unexpected plan failures. In this paper we present an abstract knowledge based approach for the diagnosis and recovery of plan action failures. Our approach associates a sentinel agent with each problem solving agent in order to monitor the problem solving agent’s interactions. The proposed approach also requires the problem solving agents to be able to report on the status of a plan’s actions.Once an exception is detected the sentinel agents start an investigation of the suspected agents. The sentinel agents collect information about the status of failed plan abstract actions and knowledge about agents’ mental attitudes regarding any failed plan. The sentinel agent then uses this abstract knowledge and the agents’ mental attitudes, to diagnose the underlying cause of the plan failure. The sentinel agent may ask the problem solving agent to retry their failed plan based on the diagnostic result.     

Facility layout design using virtual multi-agent system

This paper presents a novel approach to the facility layout design problem based on multi-agent society where agents’ interactions form the facility layout design. Each agent corresponds to a facility with inherent characteristics, emotions, and a certain amount of money, forming its utility function. An agent’s money is adjusted during the learning period by a manager agent while each agent tries to tune the parameters of its utility function in such a way that its total layout cost can be minimized in competition with others. The agents’ interactions are formed based on market mechanism. In each step, an unoccupied location is presented to all applicant agents, for which each agent proposes a price proportionate to its utility function. The agent proposing a higher price is selected as the winner and assigned to that location by an appropriate space-filling curve. The proposed method utilizes the fuzzy theory to establish each agent’s utility function. In addition, it provides a simulation environment using an evolutionary algorithm to form different interactions among the agents and makes it possible for them to experience various strategies. The experimental results show that the proposed approach achieves a lower total layout cost compared with state of the art methods.     

Electronic institutions for B2B: dynamic normative environments

The regulation of the activity of multiple autonomous entities represented in a multi-agent system, in environments with no central design (and thus with no cooperative assumption), is gaining much attention in the research community. Approaches to this concern include the use of norms in so-called normative multi-agent systems and the development of electronic institution frameworks. In this paper we describe our approach towards the development of an electronic institution providing an enforceable normative environment. Within this environment, institutional services are provided that assist agents in forming cooperative structures whose commitments are made explicit through contracts. Our normative framework borrows some concepts from contract law theory. Contracts are formalized using norms which are used by the institution while monitoring agents’ activities, thus making our normative environment dynamic. We regard the electronic institution as a means to facilitate both the creation and the enforcement of contracts between agents. A model of “institutional reality” is presented that allows for monitoring the fulfillment of norms. The paper also distinguishes our approach from other developments of the electronic institution concept. We address the application of our proposal in the B2B field, namely regarding the formation of Virtual Organizations.     

Organisational adaptation of multi-agent systems in a peer-to-peer scenario

Organisations in multi-agent systems (MAS) have proven to be successful in regulating agent societies. Nevertheless, changes in agents’ behaviour or in the dynamics of the environment may lead to a poor fulfilment of the system’s purposes, and so the entire organisation needs to be adapted. In this paper we focus on endowing the organisation with adaptation capabilities, instead of expecting agents to be capable of adapting the organisation by themselves. We regard this organisational adaptation as an assisting service provided by what we call the Assistance Layer. Our generic Two Level Assisted MAS Architecture (2-LAMA) incorporates such a layer. We empirically evaluate this approach by means of an agent-based simulator we have developed for the P2P sharing network domain. This simulator implements 2-LAMA architecture and supports the comparison between different adaptation methods, as well as, with the standard BitTorrent protocol. In particular, we present two alternatives to perform norm adaptation and one method to adapt agents’ relationships. The results show improved performance and demonstrate that the cost of introducing an additional layer in charge of the system’s adaptation is lower than its benefits.     

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.     

The Human Communication Space: Towards I-centric Communications

In the last few years, a variety of concepts for service integration and corresponding systems have been developed. On the one hand, they aim for the interworking and integration of classical telecommunications and data communications services. On the other, they are focusing on universal service access from a variety of end-user systems. Many of the technical problems, resulting from the service integration, and service personalisation have been solved during the last years. However, all these systems are driven by the concept of providing several technologies to users by keeping the peculiarity of each service.  Looking at humans’ communication behaviour and their communication space, it is obvious that human beings interact habitually in a set of contexts with their environment. The individual information preferences and needs, persons to interact with, and the set of devices controlled by each individual define their personal communication space. Following this view, a new approach is to build communication systems not on the basis of specific technologies, but on the analysis of the individual communication spaces. The result is a communication system adapted to the demands of each individual (I-centric). The communication system will act on behalf of users’ demands, reflecting recent actions to enable profiling and self-adaptation to contexts and situations.  In this paper, we introduce I-centric Communications, an approach to design communication systems that adapt themselves to the individual communication space and individual environment and situation. In this context “I” means I, or individual, “Centric” means adaptable to I requirements and a certain user environment.     

A multiagent framework for coordinated parallel problem solving

Today’s organizations, under increasing pressure on the effectiveness and the increasing need for dealing with complex tasks beyond a single individual’s capabilities, need technological support in managing complex tasks that involve highly distributed and heterogeneous information sources and several actors. This paper describes CoPSF, a multiagent system middle-ware that simplifies the development of coordinated problem solving applications while ensuring standard compliance through a set of system services and agents. CoPSF hosts and serves multiple concurrent teams of problem solving contributing both to the limitation of communication overheads and to the reduction of redundant work across teams and organizations. The framework employs (i) an interleaved task decomposition and allocation approach, (ii) a mechanism for coordination of agents’ work, and (iii) a mechanism that enables synergy between parallel teams.     

Winner determination in voting trees with incomplete preferences and weighted votes

In multiagent settings where agents have different preferences, preference aggregation can be an important issue. Voting is a general method to aggregate preferences. We consider the use of voting tree rules to aggregate agents’ preferences. In a voting tree, decisions are taken by performing a sequence of pairwise comparisons in a binary tree where each comparison is a majority vote among the agents. Incompleteness in the agents’ preferences is common in many real-life settings due to privacy issues or an ongoing elicitation process. We study how to determine the winners when preferences may be incomplete, not only for voting tree rules (where the tree is assumed to be fixed), but also for the Schwartz rule (in which the winners are the candidates winning for at least one voting tree). In addition, we study how to determine the winners when only balanced trees are allowed. In each setting, we address the complexity of computing necessary (respectively, possible) winners, which are those candidates winning for all completions (respectively, at least one completion) of the incomplete profile. We show that many such winner determination problems are computationally intractable when the votes are weighted. However, in some cases, the exact complexity remains unknown. Since it is generally computationally difficult to find the exact set of winners for voting trees and the Schwartz rule, we propose several heuristics that find in polynomial time a superset of the possible winners and a subset of the necessary winners which are based on the completions of the (incomplete) majority graph built from the incomplete profiles.     

An integrated trust and reputation model for open multi-agent systems

Trust and reputation are central to effective interactions in open multi-agent systems (MAS) in which agents, that are owned by a variety of stakeholders, continuously enter and leave the system. This openness means existing trust and reputation models cannot readily be used since their performance suffers when there are various (unforseen) changes in the environment. To this end, this paper presents FIRE, a trust and reputation model that integrates a number of information sources to produce a comprehensive assessment of an agent’s likely performance in open systems. Specifically, FIRE incorporates interaction trust, role-based trust, witness reputation, and certified reputation to provide trust metrics in most circumstances. FIRE is empirically evaluated and is shown to help agents gain better utility (by effectively selecting appropriate interaction partners) than our benchmarks in a variety of agent populations. It is also shown that FIRE is able to effectively respond to changes that occur in an agent’s environment.     

Analyzing and visualizing multiagent rewards in dynamic and stochastic domains

The ability to analyze the effectiveness of agent reward structures is critical to the successful design of multiagent learning algorithms. Though final system performance is the best indicator of the suitability of a given reward structure, it is often preferable to analyze the reward properties that lead to good system behavior (i.e., properties promoting coordination among the agents and providing agents with strong signal to noise ratios). This step is particularly helpful in continuous, dynamic, stochastic domains ill-suited to simple table backup schemes commonly used in TD(λ)/Q-learning where the effectiveness of the reward structure is difficult to distinguish from the effectiveness of the chosen learning algorithm. In this paper, we present a new reward evaluation method that provides a visualization of the tradeoff between the level of coordination among the agents and the difficulty of the learning problem each agent faces. This method is independent of the learning algorithm and is only a function of the problem domain and the agents’ reward structure. We use this reward property visualization method to determine an effective reward without performing extensive simulations. We then test this method in both a static and a dynamic multi-rover learning domain where the agents have continuous state spaces and take noisy actions (e.g., the agents’ movement decisions are not always carried out properly). Our results show that in the more difficult dynamic domain, the reward efficiency visualization method provides a two order of magnitude speedup in selecting good rewards, compared to running a full simulation. In addition, this method facilitates the design and analysis of new rewards tailored to the observational limitations of the domain, providing rewards that combine the best properties of traditional rewards.     

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.