A multi-agent system with application in project scheduling

The new economic and social dynamics increase project complexity and makes scheduling problems more difficult, therefore scheduling requires more versatile solutions as Multi Agent Systems (MAS). In this paper the authors analyze the implementation of a Multi-Agent System (MAS) considering two scheduling problems: TCPSP (Time-Constrained Project Scheduling), and RCPSP (Resource-Constrained Project Scheduling). The authors propose an improved BDI (Beliefs, Desires, and Intentions) model and present the first the MAS implementation results in JADE platform.multi-agent architecture, scheduling, project management, BDI architecture, JADE.

Strategic Distinguishability and Robust Virtual Implementation

In a general interdependent preference environment, we characterize when two payoff types can be distinguished by their rationalizable strategic choices without any prior knowledge of their beliefs and higher order beliefs. We show that two types are strategically distinguishable if and only if they satisfy a separability condition. The separability condition for each agent essentially requires that there is not too much interdependence in preferences across agents. A social choice function -- mapping payoff type profiles to outcomes -- can be robustly virtually implemented if there exists a mechanism such that every equilibrium on every type space achieves an outcome arbitrarily close to the social choice function: this definition is equivalent to requiring virtual implementation in iterated deletion of strategies that are strictly dominated for all beliefs. The social choice function is robustly measurable if strategically indistinguishable types receive the same allocation. We show that ex post incentive compatibility and robust measurability are necessary and sufficient for robust virtual implementation.Mechanism design, Virtual implementation, Robust implementation, Rationalizability, Ex-post incentive compatibility

Robust Virtual Implementation

In a general interdependent preference environment, we characterize when two payoff types can be distinguished by their rationalizable strategic choices without any prior knowledge of their beliefs and higher order beliefs. We show that two payoff types are strategically distinguishable if and only if they satisfy a separability condition. The separability condition for each agent essentially requires that there is not too much interdependence in preferences across agents. A social choice function -- mapping payoff type profiles to outcomes -- can be robustly virtu­ally implemented if there exists a mechanism such that every equilibrium on every type space achieves an outcome arbitrarily close to the social choice function. This definition is equivalent to requiring virtual implementation in iterated deletion of strategies that are strictly dominated for all beliefs. The social choice function is robustly measurable if strategically indistinguishable payoff types receive the same allocation. We show that ex post incentive compatibility and robust measurability are necessary and sufficient for robust virtual implementation.Mechanism design, Virtual implementation, Robust implementation, Rationaliz­ability, Ex-post incentive compatibility

The limits of ex post implementation

The sensitivity of Bayesian implementation to agents' beliefs about others suggests the use of more robust notions of implementation such as ex-post implementation, which requires that each agent' s strategy be optimal for every possible realization of the types of other agents. We show that the only deterministic social choice functions that are ex-post implementable in generic mechanism design frameworks with multi-dimensional signals, interdependent valuations and transferable utilities, are constant functions. In other words, deterministic ex-post implementation requires that the same alternative must be chosen irrespective of agents' signals. The proof shows that ex-post implementability of a non-trivial deterministic social choice function implies that certain rates of information substitution coincide for all agents. This condition amounts to a system of differential equations that are not satis�ed by generic valuation functions

Belief Revision in Multi-Agent Systems

The ability to respond sensibly to changing and conflicting beliefs is an integral part of intelligent agency. To this end, we outline the design and implementation of a Distributed Assumption-based Truth Maintenance System (DATMS) appropriate for controlling cooperative problem solving in a dynamic real world multi-agent community. Our DATMS works on the principle of local coherence which means that different agents can have different perspectives on the same fact provided that these stances are appropriately justified. The belief revision algorithm is presented, the meta-level code needed to ensure that all system-wide queries can be uniquely answered is described, and the DATMS’ implementation in a general purpose multi-agent shell is discussed

Robust Virtual Implementation

In a general interdependent preference environment, we characterize when two payoff types can be distinguished by their rationalizable strategic choices without any prior knowledge of their beliefs and higher order beliefs. We show that two payoff types are strategically distinguishable if and only if they satisfy a separability condition. The separability condition for each agent essentially requires that there is not too much interdependence in preferences across agents. A social choice function — mapping payoff type profiles to outcomes — can be robustly virtually implemented if there exists a mechanism such that every equilibrium on every type space achieves an outcome arbitrarily close to the social choice function. This definition is equivalent to requiring virtual implementation in iterated deletion of strategies that are strictly dominated for all beliefs. The social choice function is robustly measurable if strategically indistinguishable payoff types receive the same allocation. We show that ex post incentive compatibility and robust measurability are necessary and sufficient for robust virtual implementation

The Ontology of Intentional Agency in Light of Neurobiological Determinism: Philosophy Meets Folk Psychology

The moot point of the Western philosophical rhetoric about free will consists in examining whether the claim of authorship to intentional, deliberative actions fits into or is undermined by a one-way causal framework of determinism. Philosophers who think that reconciliation between the two is possible are known as metaphysical compatibilists. However, there are philosophers populating the other end of the spectrum, known as the metaphysical libertarians, who maintain that claim to intentional agency cannot be sustained unless it is assumed that indeterministic causal processes pervade the action-implementation apparatus employed by the agent. The metaphysical libertarians differ among themselves on the question of whether the indeterministic causal relation exists between the series of intentional states and processes, both conscious and unconscious, and the action, making claim for what has come to be known as the event-causal view, or between the agent and the action, arguing that a sort of agent causation is at work. In this paper, I have tried to propose that certain features of both event-causal and agent-causal libertarian views need to be combined in order to provide a more defendable compatibilist account accommodating deliberative actions with deterministic causation. The ‘‘agent-executed-eventcausal libertarianism’’, the account of agency I have tried to develop here, integrates certain plausible features of the two competing accounts of libertarianism turning them into a consistent whole. I hope to show in the process that the integration of these two variants of libertarianism does not challenge what some accounts of metaphysical compatibilism propose—that there exists a broader deterministic relation between the web of mental and extra-mental components constituting the agent’s dispositional system—the agent’s beliefs, desires, short-term and long-term goals based on them, the acquired social, cultural and religious beliefs, the general and immediate and situational environment in which the agent is placed, etc. on the one hand and the decisions she makes over her lifetime on the basis of these factors. While in the ‘‘Introduction’’ the philosophically assumed anomaly between deterministic causation and the intentional act of deciding has been briefly surveyed, the second section is devoted to the task of bridging the gap between compatibilism and libertarianism. The next section of the paper turns to an analysis of folk-psychological concepts and intuitions about the effects of neurochemical processes and prior mental events on the freedom of making choices. How philosophical insights can be beneficially informed by taking into consideration folk-psychological intuitions has also been discussed, thus setting up the background for such analysis. It has been suggested in the end that support for the proposed theory of intentional agency can be found in the folk-psychological intuitions, when they are taken in the right perspective

Understanding Cognitive Processes Underlying Belief Polarization and Function-Learning: Experimental and Modeling Approaches

The beliefs we hold not only influence how we seek out and perceive new information but also influence whether we take action and thus have far reaching impact on decision-making. The main goal of the present research is twofold: First, to contribute to the understanding of propagation and polarization of beliefs from a cognitive perspective by integrating experimental findings regarding confidence in climate change knowledge and cognitive modeling approaches into an agent-based belief model. Second, to outline how an implementation of a function-learning model in a cognitive architecture, based on two experiments, can contribute to a better understanding of cognitive processes underlying the understanding of non-linear functions

Reasoning about agent deliberation

We present a family of sound and complete logics for reasoning about deliberation strategies for SimpleAPL programs. SimpleAPL is a fragment of the agent programming language 3APL designed for the implementation of cognitive agents with beliefs, goals and plans. The logics are variants of PDL, and allow us to prove safety and liveness properties of SimpleAPL agent programs under different deliberation strategies. We show how to axiomatize different deliberation strategies for SimpleAPL programs, and, for each strategy we consider, prove a correspondence between the operational semantics of SimpleAPL and the models of the corresponding logic. We illustrate the utility of our approach with an example in which we show how to verify correctness properties for a simple agent program under different deliberation strategies

From SMART to agent systems development

In order for agent-oriented software engineering to prove effective it must use principled notions of agents and enabling specification and reasoning, while still considering routes to practical implementation. This paper deals with the issue of individual agent specification and construction, departing from the conceptual basis provided by the SMART agent framework. SMART offers a descriptive specification of an agent architecture but omits consideration of issues relating to construction and control. In response, we introduce two new views to complement SMART: a behavioural specification and a structural specification which, together, determine the components that make up an agent, and how they operate. In this way, we move from abstract agent system specification to practical implementation. These three aspects are combined to create an agent construction model, actSMART, which is then used to define the AgentSpeak(L) architecture in order to illustrate the application of actSMART