Normative KGP agents

We extend the logical model of agency known as the KGP model, to support agents with normative concepts, based on the roles an agent plays and the obligations and prohibitions that result from playing these roles. The proposed framework illustrates how the resulting normative concepts, including the roles, can evolve dynamically during the lifetime of the agent. Furthermore, we illustrate how these concepts can be combined with the existing capabilities of KGP agents in order to plan for their goals, react to changes in the environment, and interact with other agents. Our approach gives an executable specification of normative concepts that can be used directly for prototyping applications. Fariba Sadri is a senior lecturer at Imperial College London, from where she received her PhD. Her earlier work concentrated on integrity of deductive databases and temporal reasoning, in particular using the event calculus. In more recent years her work has been on agent technologies and multi-agent systems. She has worked on logic-based agent models, reasoning, dynamic belief revision, and inter-agent communication and negotiation for resources. She was co-awarded an EPSRC grant for research into logic-based multi-agents and was co-investigator in the EU SOCS project. Kostas Stathis is a senior lecturer at Royal Holloway, University of London and he holds a PhD from Imperial College London. His research interests are in the area of computational intelligence in general and in the intersection of computational logic and cognitive systems for social computing applications in particular. His research interests include: representation of human-computer (or computer-computer) interaction as a game; cognitive & autonomous agents; artificial agent societies; agent communication; programmable agents and agent platforms. He is a co-investigator of the EU ArguGRID project and was a co-investigator of the EU SOCS project. Francesca Toni is a senior lecturer at Imperial College London, from where she received her PhD. Her earlier work focused on abductive reasoning. In more recent years, she focused on argumentation, agent models and multi-agent systems. She has worked on computational logic-based agent models, agent reasoning, dynamic belief revision, and inter-agent communication and negotiation for resources. She has been co-ordinator of the EU SOCS project, which developed the KGP model of agency, and is coordinator of the EU ArguGRID project, on the application of argumentative agents within grid systems.     

Argumentation update in YALLA (Yet Another Logic Language for Argumentation)

This article proposes a complete framework for handling the dynamics of an abstract argumentation system. This frame can encompass several belief bases under the form of several argumentation systems, more precisely it is possible to express and study how an agent who has her own argumentation system can interact on a target argumentation system (that may represent a state of knowledge at a given stage of a debate). The two argumentation systems are defined inside a reference argumentation system called the universe which constitutes a kind of “common language”. This paper establishes three main results. First, we show that change in argumentation in such a framework can be seen as a particular case of belief update. Second, we have introduced a new logical language called YALLA in which the structure of an argumentation system can be encoded, enabling to express all the basic notions of argumentation theory (defense, conflict-freeness, extensions) by formulae of YALLA. Third, due to previous works about dynamics in argumentation we have been in position to provide a set of new properties that are specific for argumentation update.     

Qualitative criteria of admissibility for enforced agreements

It is desirable that artificial agents can help each other when they cannot achieve their goals, or when they profit from social exchanges. In this work we study coalition formation processes supported by enforced agreements and we define two qualitative criteria, the do-ut-des property and the composition property, that establish when a coalition is admissible to be formed. The do-ut-des property is based on a balance between the advantages and the burdens of an agent, when it agrees an enforced agreements. The composition property is a refinement of the do-ut-des property that takes into account also the costs and the risks deriving from the coalition formation process. Two relevant aspects distinguish our approach from the solution criteria developed in cooperative game theory. First, the do-ut-des property and the composition property are not based on an explicit utility function associated to the goals of an agent, and hance they can be used also in that cases in which the importance that agents give to their own goals is unknown. Second, a coalition has all the necessary information to establish if it satisfies the do-ut-des property or the composition property, therefore these two properties can be used in the case not all the space of possible coalitions is known. Luigi Sauro graduated in Physics at the University “Federico II” of Naples in 2001. From February 2002 to July 2002 he was collaborator at the SRA division of the IRST Institute (Trento). He got is Ph.D. in Computer Science from University of Torino in February 2006. Currently he is member of the Natural Language Processing and Agents Group, directed by prof. Leonardo Lesmo. His research interests include social reasoning, coalition formation and coordination in multiagent systems.     

Decentralized and dynamic group formation of reconfigurable agents

Adaptive group formation in dynamic environments performed by heterogeneous swarms of simple agents is an interesting research topic. In this paper we consider an unsupervised scenario where the individuals of the swarm have limited information about their environment as well as limited communication capabilities. The particular case of a multi-agent model with self-organized reconfigurable agents where the agents are confronted with a resource collection task, different movement, and group formation tactics are analyzed experimentally. It is shown that cooperation in groups is profitable for the group members and the optimal group size depends on environmental parameters. Moreover, a simple strategy based on the agents ability to measure their own workload results in an adaptive behavior that influences the size of the groups and increases the performance of the overall system.     

Generating possible intentions with constrained argumentation systems

Practical reasoning (PR), which is concerned with the generic question of what to do, is generally seen as a two steps process: (1) deliberation, in which an agent decides what state of affairs it wants to reach - that is, its desires; and (2) means-ends reasoning, in which the agent looks for plans for achieving these desires. The agent’s intentions are a consistent set of desires that are achievable together.This paper proposes the first argumentation system for PR that computes in one step the possible intentions of an agent, avoiding thus the drawbacks of the existing systems. The proposed system is grounded on a recent work on constrained argumentation systems, and satisfies the rationality postulates identified in argumentation literature, namely the consistency and the completeness of the results.     

A behavioral stock market model

Stock exchanges are modeled as nonlinear closed-loop systems where the plant dynamics is defined by known stock market regulations and the actions of agents are based on their beliefs and behavior. The decision of the agents may contain a random element, thus we get a nonlinear stochastic feedback system. The market is in equilibrium when the actions of the agents reinforce their beliefs on the price dynamics. Assuming that linear predictors are used for prediction of the price process, a stochastic approximation procedure for finding market equilibrium is described. The proposed procedure is analyzed using the theory of Benveniste et al. (Adaptive algorithms and stochastic approximations. Springer, Berlin, 1990). A simulation result is also presented.     

On the equivalence between logic programming semantics and argumentation semantics

In the current paper, we re-examine the connection between formal argumentation and logic programming from the perspective of semantics. We observe that one particular translation from logic programs to instantiated argumentation (the one described by Wu, Caminada and Gabbay) is able to serve as a basis for describing various equivalences between logic programming semantics and argumentation semantics. In particular, we are able to show equivalence between regular semantics for logic programming and preferred semantics for formal argumentation. We also show that there exist logic programming semantics (L-stable semantics) that cannot be captured by any abstract argumentation semantics.     

Leader‐following consensus problem of heterogeneous multi‐agent systems with nonlinear dynamics using fuzzy disturbance observer

This article considers the leader‐following consensus problem of heterogeneous multi‐agent systems. The proposed multi‐agent system is consisted of heterogeneous agents where each agents have their own nonlinear dynamic behavior. To overcome difficulty from heterogeneous nonlinear intrinsic dynamics of agents, a fuzzy disturbance observer is adopted. In addition, based on the Lyapunov stability theory, an adaptive control method is used to compensate the observation error caused by the difference between the unknown factor and estimated values. Two numerical examples are given to illustrate the effectiveness of the proposed method. © 2013 Wiley Periodicals, Inc. Complexity 19: 20–31, 2014     

Bipolarity in argumentation graphs: Towards a better understanding

Different abstract argumentation frameworks have been used for various applications within multi-agents systems. Among them, bipolar frameworks make use of both attack and support relations between arguments. However, there is no single interpretation of the support, and the handling of bipolarity cannot avoid a deeper analysis of the notion of support.In this paper we consider three recent proposals for specializing the support relation in abstract argumentation: the deductive support, the necessary support and the evidential support. These proposals have been developed independently within different frameworks. We restate these proposals in a common setting, which enables us to undertake a comparative study of the modellings obtained for the three variants of the support. We highlight relationships and differences between these variants, namely a kind of duality between the deductive and the necessary interpretations of the support.     

A principal–agent problem with heterogeneous demand distributions for a carbon capture and storage system

Mechanism design problems optimize contract offerings from a principal to different types of agents who have private information about their demands for a product or a service. We study the implications of uncertainty in agents’ demands on the principal’s contracts. Specifically, we consider the setting where agents’ demands follow heterogeneous distributions and the principal offers a menu of contracts stipulating quantities and transfer payments for each demand distribution. We present analytical solutions for the special case when there are two distributions each taking two discrete values, as well as a method for deriving analytical solutions from numerical solutions. We describe one application of the model in carbon capture and storage systems to demonstrate various types of optimal solutions and to obtain managerial insights.     

A probabilistic approach to modelling uncertain logical arguments

Argumentation can be modelled at an abstract level using a directed graph where each node denotes an argument and each arc denotes an attack by one argument on another. Since arguments are often uncertain, it can be useful to quantify the uncertainty associated with each argument. Recently, there have been proposals to extend abstract argumentation to take this uncertainty into account. This assigns a probability value for each argument that represents the degree to which the argument is believed to hold, and this is then used to generate a probability distribution over the full subgraphs of the argument graph, which in turn can be used to determine the probability that a set of arguments is admissible or an extension. In order to more fully understand uncertainty in argumentation, in this paper, we extend this idea by considering logic-based argumentation with uncertain arguments. This is based on a probability distribution over models of the language, which can then be used to give a probability distribution over arguments that are constructed using classical logic. We show how this formalization of uncertainty of logical arguments relates to uncertainty of abstract arguments, and we consider a number of interesting classes of probability assignments.     

Identifying Kinds of Reasoning in Collective Argumentation

We combine Peirce’s rule, case, and result with Toulmin’s data, claim, and warrant to differentiate between deductive, inductive, abductive, and analogical reasoning within collective argumentation. In this theoretical article, we illustrate these kinds of reasoning in episodes of collective argumentation using examples from one teacher’s practice. Examining different kinds of reasoning in collective argumentation can inform how students engage in generating and examining hypotheses using inductive and abductive reasoning and move toward the deductive reasoning required for proof. Mathematics educators can build on their understanding of these kinds of reasoning to support students in reasoning in productive ways.     

Interaction between intelligent agent strategies for real-time transportation planning

In this paper we study the real-time scheduling of time-sensitive full truckload pickup-and-delivery jobs. The problem involves the allocation of jobs to a fixed set of vehicles which might belong to different collaborating transportation agencies. A recently proposed solution methodology for this problem is the use of a multi-agent system where shipper agents offer jobs through sequential auctions and vehicle agents bid on these jobs. In this paper we consider such a system where both the vehicle agents and the shipper agents are using profit maximizing look-ahead strategies. Our main contribution is that we study the interrelation of these strategies and their impact on the system-wide logistical costs. From our simulation results, we conclude that the system-wide logistical costs (i) are always reduced by using the look-ahead strategies instead of a myopic strategy (10–20%) and (ii) the joint effect of two look-ahead strategies is larger than the effect of an individual strategy. To provide an indication of the savings that might be realized under centralized decision making, we benchmark our results against an integer programming approach.     

Theory of Semi-Instantiation in Abstract Argumentation

We study instantiated abstract argumentation frames of the form (S, R, I), where (S, R) is an abstract argumentation frame and where the arguments x of S are instantiated by I(x) as well formed formulas of a well known logic, for example as Boolean formulas or as predicate logic formulas or as modal logic formulas. We use the method of conceptual analysis to derive the properties of our proposed system. We seek to define the notion of complete extensions for such systems and provide algorithms for finding such extensions. We further develop a theory of instantiation in the abstract, using the framework of Boolean attack formations and of conjunctive and disjunctive attacks. We discuss applications and compare critically with the existing related literature.     

Hierarchical optimization: An introduction

Decision problems involving multiple agents invariably lead to conflict and gaming. In recent years, multi-agent systems have been analyzed using approaches that explicitly assign to each agent a unique objective function and set of decision variables; the system is defined by a set of common constraints that affect all agents. The decisions made by each agent in these approaches affect the decisions made by the others and their objectives. When strategies are selected simultaneously, in a noncooperative manner, solutions are defined as equilibrium points [13,51] so that at optimality no player can do better by unilaterally altering his choice. There are other types of noncooperative decision problems, though, where there is a hierarchical ordering of the agents, and one set has the authority to strongly influence the preferences of the other agents. Such situations are analyzed using a concept known as a Stackelberg strategy [13, 14,46]. The hierarchical optimization problem [11, 16, 23] conceptually extends the open-loop Stackelberg model toK players. In this paper, we provide a brief introduction and survey of recent work in the literature, and summarize the contributions of this volume. It should be noted that the survey is not meant to be exhaustive, but rather to place recent papers in context.     

Microscopic modeling and analysis of collective decision making: equality bias leads suboptimal solutions

We discuss a novel microscopic model for collective decision-making interacting multi-agent systems. In particular we are interested in modeling a well known phenomena in the experimental literature called equality bias, where agents tend to behave in the same way as if they were as good, or as bad, as their partner. We analyze the introduced problem and we prove the suboptimality of the collective decision-making in the presence of equality bias. Numerical experiments are addressed in the last section.     

Dialogue strategy for horizontal communication in MAS organization

A multi agent organization model defines the structure, roles, interaction ways and coordination styles of a multi agent system. Multi agent organizations may constrain the communication between included members. Organizations’ communication is the process of sending or receiving all the messages through a group of agents in order to achieve common goals. In a dialogue, agents follow some rules that define the permissive speech acts called dialogue protocol. Aiming for common goals, the dialogue strategy is the policy of agents to choose a particular speech act among the allowed ones by the protocol. In this paper a formal model for dialogue strategy for a group of agents in an organization is proposed in order to choose the most preferable speech acts. The argumentation theory is applied to the proposed method to define the values of plausible speech acts and to rank them. The algorithm finds the best option to utter and also it decreases the volume of exchanging messages. The proposed dialogue strategy is illustrated via a deliberation dialogue example in a group of agents.     

Studying teachers’ mathematical argumentation in the context of refuting students’ invalid claims

This study investigates teachers’ argumentation aiming to convince students about the invalidity of their mathematical claims in the context of calculus. 18 secondary school mathematics teachers were given three hypothetical scenarios of a student's proof that included an invalid algebraic claim. The teachers were asked to identify possible mistakes and explain how they would refute the student's invalid claims. Two of them were also interviewed. The data were analysed in terms of the content and structure of argumentation and the types of counterexamples the teachers generated. The findings show that teachers used two main approaches to refute students’ invalid claims, the use of theory and the use of counterexamples. The role of these approaches in the argumentation process was analysed by Toulmin's model and three types of reasoning emerged that indicate the structure of argumentation in the case of refutation. Concerning the counterexamples, the study shows that few teachers use them in their argumentation and in general they underestimate their value as a proof method.     

On Evolving Social Systems: Communication, Speciation and Symbiogenesis

In this paper we introduce three enhancements for evolutionary computing techniques in social environments. We describe the use of the genetic algorithm to evolve communicating rule-based systems, where each rule-based system represents an agent in a social/multi-agent environment. It is shown that the evolution of multiple cooperating agents can give improved performance over the evolution of an equivalent single agent, i.e. non-social, system. We examine the performance of two social system configurations as approaches to the control of gait in a wall climbing quadrupedal robot, where each leg of the quadruped is controlled by a communicating agent. We then introduce two social-level operators&2014;speciation and symbiogenesis&2014;which aim to reduce the amount of knowledge required a priori by automatically manipulating the system&2018;s social structure and describe their use in conjunction with the communicating rule-based systems. The reasons for implementing these kinds of operators are discussed and we then examine their performance in developing the controller of the wall-climbing quadruped. We find that the use of such operators can give improved performance over static population/agent configurations.     

CHARTING ARGUMENTATION SPACE IN CONCEPTUAL LOCALES: TOOLS AT THE BOUNDARY BETWEEN RESEARCH AND PRACTICE

We describe a procedure for developing pedagogical knowledge about the argumentation of groups of children in relation to conceptual locales. The method involved the collection of small groups of children who had made different but significant responses to diagnostic test items, the recording and analysis of their subsequent researcher-managed discourses, and the mapping of the main productive elements of argument and charting a flow through it. Two examples of the method are presented which show how children can develop argument, and how these can be charted. These charts and other tools encapsulate research knowledge on children's learning in a form designed to help teachers to plan argumentation in their classroom practice: i.e. as pedagogical content knowledge. In addition to content-focused results, we also find general teaching strategies which appear to be effective generally, i.e. across conceptual locales. We discuss the relationship of this work to the development of teacher's pedagogical content knowledge and practice.