Threats make part of the set of rhetorical arguments, which are used in negotiation dialogues when a proponent agent tries to persuade his opponent to accept a proposal more readily. When more than one threat is generated, the proponent... more
Threats make part of the set of rhetorical arguments, which are used in negotiation dialogues when a proponent agent tries to persuade his opponent to accept a proposal more readily. When more than one threat is generated, the proponent must evaluate each and select the most adequate. One way of evaluation is calculating the strength of threats, since a strong threat may quickly convince an opponent. The contribution of this paper is twofold. On the one hand, we present a mechanism of generation of threats and, on the other hand, we propose a model for calculating the strength of threats, which is based on the goal processing inside the mental state of the opponent. We propose two ways for calculating the strength of threats depending on the kind of negotiation the agent is participating. The first proposal is to be used when the agent negotiates only with one opponent, and the second when the agent negotiates with more than one opponent.
Research Interests: Artificial Intelligence, Combinatorial Optimization, Simulated Annealing, Landscape, Neural Network, and 11 moreHigher Order Thinking, Combinatorial Problems, State Space, Search Engine, Sales, TSP, Hopfield neural network, Boolean Satisfiability, Travelling Salesman Problem, Boolean Logic, and Traveling Salesperson Problem
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In this paper we propose an architecture for deliberative agents based on progressive reasoning. When an agent receives a query, it tries to satisfy it by building an answer based on its current knowledge. Depending on the available time... more
In this paper we propose an architecture for deliberative agents based on progressive reasoning. When an agent receives a query, it tries to satisfy it by building an answer based on its current knowledge. Depending on the available time or the urgency of the requirement the agent can produce answers with different levels of quality. Agents could build progressively their answers with the information they receive from perception or during the dialogue with other agents. We assume that in the real world normally it is better to receive an answer with poor quality than no answer. The answer can be good enough for the receiver or the receiver can spend more time to wait for a better answer. Autonomy implies taking the best decision with the available information, avoiding blocking situations and no action.
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This paper presents the implementation of ARQ-PROP II, a limited-depth propositional reasoner, via the compilation of its specification into an exact formulation using the satyrus platform. satyrus’ compiler takes as input the definition... more
This paper presents the implementation of ARQ-PROP II, a limited-depth propositional reasoner, via the compilation of its specification into an exact formulation using the satyrus platform. satyrus’ compiler takes as input the definition of a problem as a set of pseudo-Boolean constraints and produces, as output, the Energy function of a higher-order artificial neural network. This way, satisfiability of a formula can be associated to global optima. In the case of ARQ-PROP II, global optima is associated to Resolution-based refutation, in such a way that allows for simplified abduction and prediction to be unified with deduction. Besides experimental results on deduction with ARQ-PROP II, this work also corrects the mapping of satisfiability into Energy minima originally proposed by Gadi Pinkas.
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This paper introduces a novel approach to the specification of hard combinatorial problems as pseudo-Boolean constraints. It is shown (i) how this set of constraints defines an energy landscape representing the space state of solutions of... more
This paper introduces a novel approach to the specification of hard combinatorial problems as pseudo-Boolean constraints. It is shown (i) how this set of constraints defines an energy landscape representing the space state of solutions of the target problem, and (ii) how easy is to combine different problems into new ones mostly via the union of the corresponding constraints. Graph colouring and Traveling Salesperson Problem (TSP) were chosen as the basic problems from which new combinations were investigated. Higher-order Hopfield networks of stochastic neurons were adopted as search engines in order to solve the mapped problems.
Research Interests: Artificial Intelligence, Combinatorial Optimization, Simulated Annealing, Landscape, Neural Network, and 11 moreHigher Order Thinking, Combinatorial Problems, State Space, Search Engine, Sales, TSP, Hopfield neural network, Boolean Satisfiability, Travelling Salesman Problem, Boolean Logic, and Traveling Salesperson Problem
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