An Introduction to MultiAgent Systems.

From the Publisher:
An agent is an entity with domain knowledge, goals and actions. Multi-agent systems are a set of agents which
interact in a common environment. Multi-agent systems deal with the construction of complex systems involving multiple agents and their coordination. A multi-agent system (MAS) is a distributed computing system with
autonomous interacting intelligent agents that coordinate their actions so as to achieve its goal(s) jointly or competitively.

Introduction to Multiagent Systems

What is modal logic

Current technology is beginning to allow us to manipulate rather than just observe individual quantum phenomena. This opens up the possibility of exploiting quantum effects to perform computations beyond the scope of any classical computer. Recently Peter Shor discovered an efficient algorithm for factoring whole numbers, which uses characteristically quantum effects. The algorithm illustrates the potential power of quantum computation, as there is no known efficient classical method for solving this problem. The authors give an exposition of Shor's algorithm together with an introduction to quantum computation and complexity theory. They discuss experiments that may contribute to its practical implementation. [S0034-6861(96)00303-0]

http://www.phys.uni-sofia.bg/~svetivanov/FourierPapers/1996%20-%20(Th)%20Shor%20-%20Ekert%20-%20RevModPhys.pdf

Quantum Computation and Shor's Factoring Algorithm

Argumentation in artificial intelligence

Issues in Multiagent Resource Allocation
Yann Chevaleyre, Paul E. Dunne, Ulle Endriss, Jerome Lang, Michel Lemaitre,
 Nicolas Maudet, Julian Padget, Steve Phelps, Juan A. Rodrigues-Aguilar,
 Paulo Sousa

Abstract:
The allocation of resources within a system of autonomous agents, that
not only have preferences over alternative allocations of resources
but also actively participate in computing an allocation, is an
exciting area of research at the interface of Computer Science and
Economics. This paper is a survey of some of the most salient issues
in Multiagent Resource Allocation. In particular, we review various
languages to represent the preferences of agents over alternative
allocations of resources as well as different measures of social
welfare to assess the overall quality of an allocation. We also
discuss pertinent issues regarding allocation procedures and present
important complexity results. Our presentation of theoretical issues
is complemented by a discussion of software packages for the
simulation of agent-based market places. We also introduce four major
application areas for Multiagent Resource Allocation, namely
industrial procurement, sharing of satellite resources, manufacturing
control, and grid computing.

/pdf/issues-in-multiagent-resource-allocation-2kqgmuyht3.pdf

Issues in multiagent resource allocation

https://www.cs.ox.ac.uk/people/michael.wooldridge/pubs/aij2011a.pdf

Weighted argument systems: Basic definitions, algorithms, and complexity results

https://cgi.csc.liv.ac.uk/~tbc/publications/ulcs-01-006.pdf

Coherence in finite argument systems

argumentation may be further advanced. We stress that our aim is to focus on general areas rather than particular open questions as such: the reader who has followed the earlier exposition will have noted that a number of specific open issues have already been raised in the text. 6.1 Average case properties As discussed in Section 5.2, the lower bounds on problem complexity are worstcase, so leaving open the possibility that feasible algorithms may be available in suitable contexts. In addition to the use of restrictions on the form of instances one other approach that has been widely considered in the theory of algorithms is the study of average-case complexity. Underpinning this approach one considers a probability distribution, μ , on instances of a decision problem – often, but not invariably so, μ is the uniform distribution whereby each instance is equally likely, proceeding to define the average-case run time of an algorithm P on instances of size n of L as ∑x∈I(n) μ(x)ρ(P,x) where ρ(P,x) is the run-time of P on instance x. Formal definitions of average-case complexity classes may be found in [36]. To date surprisingly little work has been carried out concerning the application of average-case methods to decision problems in AFs either in terms of algorithmic development or in considering the limitations of such approaches. It remains open to what extent techniques such as those applied to other intractable problems, e.g., [1] for the NP–complete Hamiltonian cycle problem, or [46] for CNF satisfiability could be replicated in AF settings. Of some relevance to such approaches are so-called “phase-transition” effects, which received much attention in the mid-late 1990s as potential indicators of factors separating tractable and intractable classes of problem instances, e.g., the studies of random CNF-SAT from [37, 40]. Analytic studies of such effects appears to indicate connections between suitable witnessing structures, e.g., satisfying assignment, being present “almost certainly” and the performance of algorithms to identify such structures. Of some interest in the context of AF semantics are the results of [41, 17] which give conditions ensuring that a random AF “almost certainly” has a stable extension. There has as yet, however, been no detailed study of the implications of these results for fast on average methods for identifying or enumerating stable extensions. In the same way that the analyses of [41, 17] relate 102 Paul E. Dunne and Michael Wooldridge to the existence of stable extensions in AFs, it would be of some interest to examine to consider existence properties of other solution structures in random AFs and algorithmic consequences. 6.2 Approaches to dynamic updates An important feature of the argumentation forms discussed so far is that, in practice, these are not static systems: typically an AF, 〈A,R〉, represents only a “snapshot” of the environment, and, as further facts, information and opinions emerge the form of the initial view may change significantly in order to accommodate these. For example, additional arguments may have to be considered so changing A; existing attacks may cease to apply and new attacks (arising from changes to A) come into force. It is clear that accounting for such dynamic aspects raises a number of issues in terms of assessing the acceptability status of individual arguments. As with the study of average-case properties, the treatment of algorithms and complexity issues relating to determining argument status in dynamically changing environments has been somewhat neglected. Thus, given 〈A,R〉 and S ⊆A for which S ∈ Es(〈A,R〉) according to some semantics s, natural decision questions are: does x ∈ S continue to be credulously accepted (w.r.t. to semantics s) in the AF 〈B,S〉 where B results by removing some arguments from A and replacing these; similarly T modifies the attack relation R.

Paul E. Dunne

Papers

Argumentation in artificial intelligence

Issues in multiagent resource allocation

Weighted argument systems: Basic definitions, algorithms, and complexity results

Coherence in finite argument systems

Complexity of Abstract Argumentation