Allocating educational resources through happiness maximization and traditional CSP approach

This is an electronic version of the paper presented at the 4th International Conference on Software and Data Technologies, held in Sofia on 2009An instance of an Educational Resources Allocation (ERA) problem is the distribution of a set of students in different laboratories. This can be a complex and dynamic problem if non-quantitative considerations (i.e. how close the final allocation is to the student preferences or desires) are involved in the decision process. Traditionally, different approaches based on Constraint-Satisfaction techniques and Multi-agent negotiation have been applied to the general problem of Resource Allocation. This paper shows how a Multi-agent approach can be used to model and simulate the assignment of sets of students to several predefined laboratories, by using their preferences to guide the allocation process. This approach aims at finding new solutions that try to satisfy individual student needs with no knowledge about the general allocation problem. The paper shows some experimental results and a comparison, between a CSP-based solution modeled in CHOCO, a CSP Java-based library, and a Multi-agent model implemented using MASON, a multi-agent simulation platform.This work has been supported by research projects TIN2007-65989 and TIN2007-64718. We also thank IBM for its support to the Linux Reference Cente

Using Pre-Computed Knowledge for Goal Allocation in Multi-Agent Planning

Many real-world robotic scenarios require performing task planning to decide courses of actions to be executed by (possibly heterogeneous) robots. A classical centralized planning approach has to find a solution inside a search space that contains every possible combination of robots and goals. This leads to inefficient solutions that do not scale well. Multi-Agent Planning (MAP) provides a new way to solve this kind of tasks efficiently. Previous works on MAP have proposed to factorize the problem to decrease the planning effort i.e. dividing the goals among the agents (robots). However, these techniques do not scale when the number of agents and goals grow. Also, in most real world scenarios with big maps, goals might not be reached by every robot so it has a computational cost associated. In this paper we propose a combination of robotics and planning techniques to alleviate and boost the computation of the goal assignment process. We use Actuation Maps (AMs). Given a map, AMs can determine the regions each agent can actuate on. Thus, specific information can be extracted to know which goals can be tackled by each agent, as well as cheaply estimating the cost of using each agent to achieve every goal. Experiments show that when information extracted from AMs is provided to a multi-agent planning algorithm, the goal assignment is significantly faster, speeding-up the planning process considerably. Experiments also show that this approach greatly outperforms classical centralized planning.This work has been partially funded by FEDER/ Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación/TIN2017-88476-C2-2-R and MINECO/TIN2014-55637-C2-1-R. I has been also financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme within project >, and by National Funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UID/EEA/50014/2013, and FCT grant SFRH/BD/52158/2013 through Carnegie Mellon Portugal Program

Multi-Unit Auctions to Allocate Water Scarcity Simulating Bidding Behaviour with Agent Based Models

Multi-unit auctions are promising mechanisms for the reallocation of water. The main advantage of such auctions is to avoid the lumpy bid issue. However, there is great uncertainty about the best auction formats when multi-unit auctions are used. The theory can only supply the structural properties of equilibrium strategies and the multiplicity of equilibria makes comparisons across auction formats difficult. Empirical studies and experiments have improved our knowledge of multi- unit auctions but they remain scarce and most experiments are restricted to two bidders and two units. Moreover, they demonstrate that bidders have limited rationality and learn through experience. This paper constructs an agent-based model of bidders to compare the performance of alternative auction formats under circumstances where bidders submit continuous bid supply functions and learn over time to adjust their bids to improve their net incomes. We demonstrate that under the generalized Vickrey, simulated bids converge towards truthful bids as predicted by the theory and that bid shading is the rule for the uniform and discriminatory auctions. Our study allows us to assess the potential gains from agent-based modelling approaches in the assessment of the dynamic performance of multi-unit procurement auctions. Some recommendations on the desirable format of water auctions are provided.Multi-unit auctions, Learning, Multi-agent models, Water allocation

Centralised Versus Market-Based Control Under Environment Uncertainty: Case of the Mobile Task Allocation Problem (MTAP)

This paper aims at comparing the centralised versus the market-based approach. This is done in the context of the mobile task allocation problem (MTAP) from the perspective of environmental uncertainty. MTAP is defined as an optimization problem for planning the assignment of service tasks to mobile workers. Environmental uncertainty is introduced through the injection of stochastic tasks and dynamic travel delays. A multi-agent simulator is employed to experiment the behaviour of each approach in reaction to different uncertainty levels. Preliminary results suggest a tentative conceptual model to evaluate the suitability of each approach to address MTAP in function of uncertainty. It is suggested that uncertainty’s effect on achieved performance is moderated by the timeliness of decision making, workers’ degree of local knowledge, and problem’s complexity and size

Reallocation Problems in Agent Societies: A Local Mechanism to Maximize Social Welfare

Resource reallocation problems are common in real life and therefore gain an increasing interest in Computer Science and Economics. Such problems consider agents living in a society and negotiating their resources with each other in order to improve the welfare of the population. In many studies however, the unrealistic context considered, where agents have a flawless knowledge and unlimited interaction abilities, impedes the application of these techniques in real life problematics. In this paper, we study how agents should behave in order to maximize the welfare of the society. We propose a multi-agent method based on autonomous agents endowed with a local knowledge and local interactions. Our approach features a more realistic environment based on social networks, inside which we provide the behavior for the agents and the negotiation settings required for them to lead the negotiation processes towards socially optimal allocations. We prove that bilateral transactions of restricted cardinality are sufficient in practice to converge towards an optimal solution for different social objectives. An experimental study supports our claims and highlights the impact of a realistic environment on the efficiency of the techniques utilized.Resource Allocation, Negotiation, Social Welfare, Agent Society, Behavior, Emergence

A winner determination algorithm for multi-unit combinatorial auctions with reserve prices

Combinatorial auction mechanisms have been used in many applications such as resource and task allocation, planning and time scheduling in multi-agent systems, in which the items to be allocated are complementary or substitutable. The winner determination in combinatorial auction itself is a NP-complete problem, and has attracted many attentions of researchers world wide. Some outstanding achievements have been made including CPLEX and CABOB algorithms on this topic. To our knowledge, the research into multi-unit combinatorial auctions with reserve prices considered is more or less ignored. To this end, we present a new algorithm for multi-unit combinatorial auctions with reserve prices, which is based on Sandholm\u27s work. An efficient heuristic function is developed for the new algorithm. Experiments have been conducted. The experimental results show that auctioneer agent can find the optimal solution efficiently for a reasonable problem scale with our algorithm. <br /

A Review on Intelligent Agent Systems

Multi-agent system (MAS) is a common way of exploiting the potential power of agent by combining many agents in one system. Each agent in a multivalent system has incomplete information and is in capable of solving entire problem on its own. Multi-agent system offers modularity. If a problem domain is particularly complex, large and contain uncertainty, then the one way to address, it to develop a number of functional specific and modular agent that are specialized at solving various problems individually. It also consists of heterogeneous agents implemented by different tool and techniques. MAS can be defining as loosely coupled network of problem solvers that interact to solve problems that are beyond the individual capabilities or knowledge of each problem solver. These problem solvers, often ailed agent are autonomous and can be heterogeneous in nature. MAS is followed by characteristics, Future application, What to be change, problem solving agent, tools and techniques used, various architecture, multi agent applications and finally future Direction and conclusion. Various Characteristics are limited viewpoint, effectively, decentralized; computation is asynchronous, use of genetic algorithms. It has some drawbacks which must be change to make MAS more effective. In the session of problem solving of MAS, the agent performance measure contains many factors to improve it like formulation of problems, task allocation, organizations. In planning of multivalent this paper cover self-interested multivalent interactions, modeling of other agents, managing communication, effective allocation of limited resources to multiple agents with managing resources. Using of tool, to make the agent more efficient in task that are often used. The architecture o MAS followed by three layers, explore, wander, avoid obstacles respectively. Further different and task decomposition can yield various architecture like BDI (Belief Desire Intension), RETSINA. Various applications of multi agent system exist today, to solve the real-life problems, new systems are being developed two distinct categories and also many others like process control, telecommunication, air traffic control, transportation systems, commercial management, electronic commerce, entertainment applications, medical applications. The future aspect of MAS to solve problems that are too large, to allow interconnection and interoperation of multiple existing legacy systems etc

Managing Social Influences through Argumentation-Based Negotiation

Social influences play an important part in the actions that an individual agent may perform within a multi-agent society. However, the incomplete knowledge and the diverse and conflicting influences present within such societies, may stop an agent from abiding by all its social influences. This may, in turn, lead to conflicts that the agents need to identify, manage, and resolve in order for the society to behave in a coherent manner. To this end, we present an empirical study of an argumentation-based negotiation (ABN) approach that allows the agents to detect such conflicts, and then manage and resolve them through the use of argumentative dialogues. To test our theory, we map our ABN model to a multi-agent task allocation scenario. Our results show that using an argumentation approach allows agents to both efficiently and effectively manage their social influences even under high degrees of incompleteness. Finally, we show that allowing agents to argue and resolve such conflicts early in the negotiation encounter increases their efficiency in managing social influences

`Why didn't you allocate this task to them?' Negotiation-Aware Task Allocation and Contrastive Explanation Generation

Task-allocation is an important problem in multi-agent systems. It becomes more challenging when the team-members are humans with imperfect knowledge about their teammates' costs and the overall performance metric. While distributed task-allocation methods let the team-members engage in iterative dialog to reach a consensus, the process can take a considerable amount of time and communication. On the other hand, a centralized method that simply outputs an allocation may result in discontented human team-members who, due to their imperfect knowledge and limited computation capabilities, perceive the allocation to be unfair. To address these challenges, we propose a centralized Artificial Intelligence Task Allocation (AITA) that simulates a negotiation and produces a negotiation-aware task allocation that is fair. If a team-member is unhappy with the proposed allocation, we allow them to question the proposed allocation using a counterfactual. By using parts of the simulated negotiation, we are able to provide contrastive explanations that providing minimum information about other's costs to refute their foil. With human studies, we show that (1) the allocation proposed using our method does indeed appear fair to the majority, and (2) when a counterfactual is raised, explanations generated are easy to comprehend and convincing. Finally, we empirically study the effect of different kinds of incompleteness on the explanation-length and find that underestimation of a teammate's costs often increases it.Comment: First two authors are equal contributor