Annals of Mathematics and Artificial Intelligence最新文献

Digital Twins (DTs) are emerging as a fundamental brick of engineering Cyber-Physical Systems (CPSs), but their notion is still mostly bound to specific business domains (e.g. manufacturing), goals (e.g. product design), or applications (e.g. the Internet of Things). As such, their value as general purpose engineering abstractions is yet to be fully revealed. In this paper, we relate DTs with agents and multiagent systems, as the latter are arguably the most rich abstractions available for the engineering of complex socio-technical and CPSs, and the former could both fill in some gaps in agent-oriented software engineering and benefit from an agent-oriented interpretation—in a cross-fertilisation journey.

Disaster response is a major challenge given the social and economic impact on the communities affected by disaster incidents. We investigate how coalition formation can be used for the problem of forming a hierarchy of resources (e.g., personnel responding to the incident). As a case study, we consider the roaring river flood scenario and model the Incident Command System (ICS) framework—providing guidelines on cooperatively responding to disaster incidents. Our approach is based on sequential characteristic-function games induced by size-based valuation structures. We show that this approach can deliver a hierarchy as required by the Operations Section of the ICS and provides a promising way to generate practical solutions for some realistic disaster scenarios.

Negotiation is a key form of interaction in multi-agent systems. Negotiation enables agents to come to a mutual agreement about a goal or plan of action. Current negotiation approaches use traditional interaction protocols which do not capture the normative meaning of interactions and often restrict agent autonomy. These traditional negotiation approaches also have difficulty specifying accountability. This paper seeks to address this gap in maintaining autonomy and capturing accountability during negotiation through the use of normative commitments. We propose Nala, a commitment-based negotiation semantics. Nala uses commitments to provide normative meaning to agent interactions. The nature of commitments support in capturing accountability through the violation of created commitments. We illustrate Nala’s usage via a case study using a game scenario where agents participate in negotiation to bring about their goals in a research constrained environment.

In this paper, we study a geometrical inequality conjecture which states that for any four points on a hemisphere with the unit radius, the largest sum of distances between the points is (4+4sqrt{2}), the best configuration is a regular square inscribed to the equator, and for any five points, the largest sum is (5sqrt{5+2sqrt{5}}) and the best configuration is the regular pentagon inscribed to the equator. We prove that the conjectured configurations are local optimal, and construct a rectangular neighborhood of the local maximum point in the related feasible set, whose size is explicitly determined, and prove that (1): the objective function is bounded by a quadratic polynomial which takes the local maximum point as the unique critical point in the neighborhood, and (2): the remaining part of the feasible set can be partitioned into a finite union of a large number of very small cubes so that on each small cube, the conjecture can be verified by estimating the objective function with exact numerical computation. We also explain the method for constructing the neighborhoods and upper-bound quadratic polynomials in detail and describe the computation process outside the constructed neighborhoods briefly.

Weighted voting games are a well-studied class of succinct simple games that can be used to model collective decision-making in, e.g., legislative bodies such as parliaments and shareholder voting. Power indices [1,2,3,4] are used to measure the influence of players in weighted voting games. In such games, it has been studied how a distinguished player’s power can be changed, e.g., by merging or splitting players (the latter is a.k.a. false-name manipulation) [5, 6], by changing the quota [7], or via structural control by adding or deleting players [8]. We continue the work on the structural control initiated by Rey and Rothe [8] by solving some of their open problems. In addition, we also modify their model to a more realistic setting in which the quota is indirectly changed during the addition or deletion of players (in a different sense than that of Zuckerman et al. [7] who manipulate the quota directly without changing the set of players), and we study the corresponding problems in terms of their computational complexity.

In this paper we present two algorithms for computing the g-asymptotes or generalized asymptotes, of a plane algebraic curve, (mathscr {C}), implicitly or parametrically defined. The asymptotes of a curve (mathscr {C}) reflect the status of (mathscr {C}) at points with sufficiently large coordinates. It is well known that an asymptote of a curve (mathscr {C}) is a line such that the distance between (mathscr {C}) and the line approaches zero as they tend to infinity. However, a curve (mathscr {C}) may have more general curves than lines describing the status of (mathscr {C}) at infinity. These curves are known as g-asymptotes or generalized asymptotes. The pseudocodes of these algorithms are presented, as well as the corresponding implementations. For this purpose, we use the algebra software Maple. A comparative analysis of the algorithms is carried out, based on some properties of the input curves and their results to analyze the efficiency of the algorithms and to establish comparative criteria. The results presented in this paper are a starting point to generalize this study to surfaces or to curves defined by a non-rational parametrization, as well as to improve the efficiency of the algorithms. Additionally, the methods developed can provide a new and different approach in prediction (regression) or classification algorithms in the machine learning field.

Illustrations are only rarely formal components of mathematical proofs, however they are often very important for understanding proofs. Illustrations are almost unavoidable in geometry, and in many other fields illustrations are helpful for carrying ideas in a more suitable way than via words or formulas. The question is: if we want to automate theorem proving, can we automate creation of corresponding illustrations too? We report on a new, generic, simple, and flexible approach for automated generation of illustrated proofs. The proofs are generated using Larus, an automated prover for coherent logic, and corresponding illustrations are generated in the GCLC language. Animated illustrations are also supported.

This work presents several new and efficient algorithms that can be used by negotiating agents to explore very large outcome spaces. The proposed algorithms can search for bids close to a utility target or above a utility threshold, and for win-win outcomes. While doing so, these algorithms strike a careful balance between being rapid, accurate, diverse, and scalable, allowing agents to explore spaces with as many as (10^{250}) possible outcomes on very run-of-the-mill hardware. We show that our methods can be used to respond to the most common search queries employed by (87%) of all agents from the Automated Negotiating Agents Competition between 2010 and 2021. Furthermore, we integrate our techniques into negotiation platform GeniusWeb in order to enable existing state-of-the-art agents (and future agents) to handle very large outcome spaces.