We present a knowledge representation framework with an associated run-time support infrastructure that is able to compute, for the benefit of the members of a norm-governed multi-agent system, physically possible and/or permitted actions current at each time, as well as sanctions that should be applied to violations of prohibitions. Experimental results on a benchmark scenario indicate how by distributing norms we can provide run-time support to large-scale, norm-governed multi-agent systems.

This paper addresses the problem of collective decision making in the case where the agents' preferences are represented by CP-nets (Conditional Preference Networks). Most existing works either do not consider the computational issues, or depend on a strong assumption that all the agents share a common preferential independence structure. To this end, this paper proposes an efficient approach, called CDMCP (Collective Decision Making with CP-nets), for aggregating multiple agents’ preferences according to majority rule. The proposed approach allows the agents to have different preferential independence structures and is computationally efficient.

The applicability conditions of legal Norms regulating computer systems can be modelled in different rules very often refer to these institutional concepts, rather ways, see, for example, (Boella, van der Torre, and than to so called brute facts. To simplify the notation we refer Verhagen 2008). If norms are represented by hard constraints, to the former as constitutive rules, and the latter simply then computer systems are designed to avoid violations.

A wide range of problems, from contingent and multiagent planning to process/service orchestration, can be viewed as games. In many of these, it is natural to spec- ify the possible behaviors procedurally. In this paper, we develop a logical framework for specifying these types of problems/games based on the situation calculus and ConGolog. The framework incorporates game-theoretic path quantifiers as in ATL. We show that the framework can be used to model such problems in a natural way. We also show how verification/synthesis techniques can be used to solve problems expressed in the framework. In particular, we develop a method for dealing with infinite state settings using fixpoint approximation and “characteristic graphs”.

This is a case-study in knowledge representation. We analyze the 'one hundred prisoners and a lightbulb' puzzle. In this puzzle it is relevant what the agents (prisoners) know, how their knowledge changes due to observations, and how they affect the state of the world by changing facts, i.e., by their actions. These actions depend on the history of previous actions and observations. Part of its interest is that all actions are local, i.e. not publicly observable, and part of the problem is therefore how to disseminate local results to other agents, and make them global. The various solutions to the puzzle are presented as protocols (iterated functions from agent's local states, and histories of actions, to actions). The computational aspect is about average runtime termination under conditions of random ('fair') scheduling. The paper consists of three parts. First, we present different versions of the puzzle, and their solution. This includes a probabilistic version, and a version assuming synchronicity (the interval between prisoners' interrogations is known). The latter is very informative for the prisoners, and allows different protocols (with faster expected termination). Then, we model the puzzle in an epistemic logic incorporating dynamic operators for the effects of information changing events. Such events include both informative actions, where agents become more informed about the non-changing state of the world, and factual changes, wherein the world and the facts describing it change themselves as well. Finally, we give the expected termination results of several protocols when assuming random scheduling. This paper integrates the literature and presents novel contributions. Novel are: Firstly, Protocol 2 and Protocol 4. Secondly, the modelling in dynamic epistemic logic in its entirety - we do not know of a case study that combines factual and informational dynamics in a setting of non-public events, or of a similar proposal to handle asynchronous behaviour in a dynamic epistemic logic. Thirdly, our computational results on Protocol 2 and results from the manuscript from author Wu.

Existing action calculi provide rich, declarative formalisms for reasoning about actions. BDI-based programming languages like AgentSpeak, on the other hand, are procedural and geared towards practical applications of cognitive agents. In this paper, we close the gap between these two lines of research by integrating action calculi and AgentSpeak programs. Specifically, we develop a new and purely declarative semantics for AgentSpeak, which paves the way for combining this language with any suitable action calculus in a strictly modular fashion. As the main technical result, we prove that the new declarative semantics is correct wrt. the standard operational semantics for AgentSpeak. This provides the basis for a modular integration of a BDI-based agent programming language with sophisticated methods for reasoning about actions.

We often deal with dynamic worlds where actions are executed by agents and events may happen. Example of such worlds range from virtual worlds such as the world of a database to robots and humans in physical worlds. To understand the dynamics of such worlds as well as to be able to assert some control over such worlds one needs to reason about the actions and events and how they may change the world. In this invited talk we will present some of the important results in this field and present some future directions. In particular, we will discuss how theories and results from reasoning about actions and change can be combined with theories and results in dynamic epistemic logics to obtain a unified theory of multi-agent actions.

The Simple Temporal Problem (STP) is a popular representation for solving centralized scheduling and planning problems. When scheduling agents are associated with different users who need to coordinate some of their activities, however, considerations such as privacy and scalability suggest solving the joint STP in a more distributed manner. Building on recent advances in STP algorithms that exploit loosely-coupled problem structure, this paper develops and evaluates algorithms for solving the multiagent STP. We define a partitioning of the multiagent STP with provable privacy guarantees, and show that our algorithms can exploit this partitioning while still finding the tightest consistent bounds on timepoints that must be coordinated across agents. We also demonstrate empirically that our algorithms can exploit concurrent computation, leading to solution time speed-ups over state-of-the-art centralized approaches, and enabling scalability to problems involving larger numbers of loosely-coupled agents.

We develop multiattribute auctions that accommodate generalized additive independent (GAI) preferences. We propose an iterative auction mechanism that maintains prices on potentially overlapping GAI clusters of attributes, thus decreases elicitation and computational burden, and creates an open competition among suppliers over a multidimensional domain. Most significantly, the auction is guaranteed to achieve surplus which approximates optimal welfare up to a small additive factor, under reasonable equilibrium strategies of traders. The main departure of GAI auctions from previous literature is to accommodate non-additive trader preferences, hence allowing traders to condition their evaluation of specific attributes on the value of other attributes. At the same time, the GAI structure supports a compact representation of prices, enabling a tractable auction process. We perform a simulation study, demonstrating and quantifying the significant efficiency advantage of more expressive preference modeling. We draw random GAI-structured utility functions with various internal structures, generate additive functions that approximate the GAI utility, and compare the performance of the auctions using the two representations. We find that allowing traders to express existing dependencies among attributes improves the economic efficiency of multiattribute auctions.

We present DCL-PC: a logic for reasoning about how the abilities of agents and coalitions of agents are altered by transferring control from one agent to another. The logical foundation of DCL-PC is CL-PC, a logic for reasoning about cooperation in which the abilities of agents and coalitions of agents stem from a distribution of atomic Boolean variables to individual agents -- the choices available to a coalition correspond to assignments to the variables the coalition controls. The basic modal constructs of DCL-PC are of the form `coalition C can cooperate to bring about phi'. DCL-PC extends CL-PC with dynamic logic modalities in which atomic programs are of the form `agent i gives control of variable p to agent j'; as usual in dynamic logic, these atomic programs may be combined using sequence, iteration, choice, and test operators to form complex programs. By combining such dynamic transfer programs with cooperation modalities, it becomes possible to reason about how the power of agents and coalitions is affected by the transfer of control. We give two alternative semantics for the logic: a `direct' semantics, in which we capture the distributions of Boolean variables to agents; and a more conventional Kripke semantics. We prove that these semantics are equivalent, and then present an axiomatization for the logic. We investigate the computational complexity of model checking and satisfiability for DCL-PC, and show that both problems are PSPACE-complete (and hence no worse than the underlying logic CL-PC). Finally, we investigate the characterisation of control in DCL-PC. We distinguish between first-order control -- the ability of an agent or coalition to control some state of affairs through the assignment of values to the variables under the control of the agent or coalition -- and second-order control -- the ability of an agent to exert control over the control that other agents have by transferring variables to other agents. We give a logical characterisation of second-order control.