Ziad Kobti

Knowledge Generation for Improving Simulations in UCT for General Game Playing

General Game Playing (GGP) aims at developing game playing agents that are able to play a variety of games and, in the absence of pre-programmed game specific knowledge, become proficient players. Most GGP players have used standard tree-search techniques enhanced by automatic heuristic learning. The UCT algorithm, a simulation-based tree search, is a new approach and has been used successfully in GGP. However, it relies heavily on random simulations to assign values to unvisited nodes and selecting nodes for descending down a tree. This can lead to slower convergence times in UCT. In this paper, we discuss the generation and evolution of domain-independent knowledge using both state and move patterns. This is then used to guide the simulations in UCT. In order to test the improvements, we create matches between a player using standard the UCT algorithm and one using UCT enhanced with knowledge.

THE COEVOLUTION OF GROUP SIZE AND LEADERSHIP: AN AGENT-BASED PUBLIC GOODS MODEL FOR PREHISPANIC PUEBLO SOCIETIES

We present an agent-based model for voluntaristic processes allowing the emergence of leadership in small-scale societies, parameterized to apply to Pueblo societies of the northern US Southwest between AD 600 and 1300. We embed an evolutionary public-goods game in a spatial simulation of household activities in which agents, representing households, decide where to farm, hunt, and locate their residences. Leaders, through their work in monitoring group members and punishing defectors, can increase the likelihood that group members will cooperate to achieve a favorable outcome in the public-goods game. We show that under certain conditions households prefer to work in a group with a leader who receives a share of the groups productivity, rather than to work in a group with no leader. Simulation produces outcomes that match reasonably well those known for a portion of Southwest Colorado between AD 600 and 900. We suggest that for later periods a model incorporating coercion, or inter-group competition, or both, and one in which tiered hierarchies of leadership can emerge, would increase the goodness-of-fit.

A Multi-Population Cultural Algorithm for Community Detection in Social Networks☆

Abstract Social networks can be viewed as a reflection of the real world which can be studied to gain insight into the real life societies and events. During the last decade, community detection as a fundamental part of social network analysis has been explored widely, however because of the complex nature of the network, it is still an open problem. In this paper, we propose a knowledge-based evolutionary algorithm to solve this problem by using a multi-population cultural algorithm. In our algorithm, knowledge is extracted from the network to guide the search direction and find the optimal solution. Meanwhile, in each step, the knowledge is updated based on the current states of the network. The results of comparison between our method and other well-known algorithms show that our algorithm is capable to find the true communities faster and more accurately than the others.

Heterogeneous Multi-Population Cultural Algorithm

In this article, a new architecture for Cultural Algorithms is proposed. The new architecture incorporates a number of sub-populations such that each sub-population is designed to optimize different parameters. According to the assigned parameters, each sub-population is a set of partial solutions which are managed by a local CA. Local CAs do not communicate with each other directly. In this architecture, a shared belief space is considered to record the best parameters. Local CAs send their best partial solutions to the belief space every generation. The belief space then updates its record of best parameters which will be used later by local CAs to evaluate their partial solutions. Due to incorporating a number of heterogeneous sub-populations, the proposed architecture is called Heterogeneous Multi-Population Cultural Algorithm (HMP-CA). Additionally, a local search heuristic is proposed to speed up the convergence of HMP-CA. The proposed HMP-CA is evaluated using a number of numerical optimization benchmark functions. The results show that the HMP-CA without the local search offers competitive results compared to the state-of-the-art methods and incorporating the proposed local search heuristic makes the proposed HMP-CA more efficient such that it outperforms all the state-of-the-art methods.

A memetic algorithm for job shop scheduling using a critical-path-based local search heuristic

In this article, a new memetic algorithm has been proposed to solve job shop scheduling problems (JSSPs). The proposed method is a genetic-algorithm-based approach combined with a local search heuristic. The proposed local search heuristic is based on critical operations. It removes the critical operations and reassigns them to a new position to improve the fitness value of the schedule. Moreover, in this article, a new fitness function is introduced for JSSPs. The new fitness function called priority-based fitness function is defined in three priority levels to improve the selection procedure. To show the generality of our proposed method, we apply it to three different types of job scheduling problems including classical, flexible and multi-objective flexible JSSPs. The experiment results show the efficiency of the proposed fitness function. In addition, the results show that incorporating local search not only offers better solutions but also improves the convergence rate. Compared to the state-of-the-art algorithms, the proposed method outperforms the existing methods in classical JSSPs and offers competitive solutions in other types of scheduling problems.

Towards a Unified Data Management and Decision Support System for Health Care

We report on progress in development of a unified data management and decision support system, UDMDSS, for application to injury prevention in health care. Our system is based on a modular architecture which supports real-time web-base desktop and mobile data acquisition, semantic data models and queries, Bayesian statistical analysis, artificial intelligence agent-based techniques to assist in modelling and simulation, subjective logic for conditional reasoning with uncertainty, advanced reporting capabilities and other features. This research work is being conducted within a multi-disciplinary team of researchers and practitioners and has been applied to a Canadian national study on child safety in automobiles and also in the context of patient falls in a hospital.

Simulating the effect of emotional stress on task performance using OCC

In this study we design and implement an artificial emotional response algorithm using the Ortony, Clore and Collins theory in an effort to understand and better simulate the response of intelligent agents in the presence of emotional stress. We first develop a general model to outline a generic emotional agent behaviour. Agents are then socially connected and surrounded by objects, or other actors, that trigger various emotions. A case study is built using a basic hospital model where nurse servicing patients interact in various static and dynamic emotional scenarios. The simulated results show that increase in emotional stress leads to higher error rates in nurse task performance.

The effect of kinship cooperation learning strategy and culture on the resilience of social systems in the village multi-agent simulation

The multi-agent village simulation was initially developed to examine the settlement and farming practices of prehispanic Pueblo Indians of the Central Mesa Verde region of Southwest Colorado (Kohler, 2000; Kohler et al.). The original model of Kohler was used to examine whether drought alone was responsible for the departure of the prehispanic Puebloan people from the Four Corners region after 700 years of occupation. The results suggested that other factors besides precipitation were important. We then proceeded to add economic factors into the simulation, first allowing agents to engage in reciprocal exchanges between kin. This resulted in larger populations, more complex social networks, and more resilient systems. However, the exchange was done randomly and individuals did not remember the transactions. In This work we explicitly embed the reciprocal exchange process within a cultural algorithm, where individual agents can remember individuals that they have cooperated with. Also, in the cultural space the group can learn generalizations about what kind of relative is likely to successfully respond to a request. These generalizations are used to drive changes in requestor behavior. The results of this approach produced an even larger and more complex system exhibiting greater dependence on hub nodes that are sensitive to precipitation.

Evolution of artifact capabilities

The subject of artifact or tool use is considered in many fields to be a vital area of research in the study of general human competence. Recently in artificial intelligence, formalizations of the mental attitudes of intentional agents have been extended to include agent capabilities with respect to artifacts or tools. We consider understanding how these individual capabilities are learned and how they evolve as important steps towards formally defining, representing and implementing complex group capabilities. In this paper, a theoretical model for artifact capability is extended to incorporate evolution and learning through exploratory methods. A representation of artifacts and the cognition of a rational agent that can learn artifact use are provided. Supervised learning is assumed and combined with historical knowledge and genetic algorithms to provide an implementation of a multi-agent simulation. The simulation is built to support an agent with the ability to learn an artifact capability through observations of its own behavior, as well as through observations of other agents in a social environment. Results obtained from the simple yet practical approach, show that learned use of artifacts outperforms random use and rational agents can learn artifact use more efficiently as a social species than on their own.

A Machine Operation Lists based Memetic Algorithm for Job Shop Scheduling

In this article, a new Memetic Algorithm (MA) has been proposed to solve Job Shop Scheduling Problems. The proposed MA is based on Machine Operation Lists (MOL), which is the exact sequence of operations for each machine. Machine Operation Lists representation is a modification of Preference List-Based representation. Linear Order Crossover (LOX) and Random operations are first considered as crossover and mutation operators for the proposed MA. Local Search heuristic (LS) of the proposed MA reconsiders all the operations of a job. It chooses a job and removes all of its operations and finally reassigns them again one by one in their sequencing order to improve the fitness value of the schedule. The proposed algorithm has been applied on the well-known benchmark of classical Job Shop Scheduling Problems (JSSP). Comparing it with the existing methods shows that the proposed MA and the proposed Genetic Algorithm (GA) without LS are effective in JSSP. Moreover, comparing the results of MA and GA shows that using LS not only improves the final results but also helps GA to converge to the final solution.