Why cognitivism?

Intention Cognitivism – the doctrine that intending to V entails, or even consists in, believing that one will V – is an important position with potentially wide-ranging implications, such as a revisionary understanding of practical reason, and a vindicating explanation of 'Practical Knowledge'. In this paper, I critically examine the standard arguments adduced in support of IC, including arguments from the parity of expression of intention and belief; from the ability to plan around one's intention; and from the explanation provided by the thesis for our knowledge of our intentional acts. I conclude that none of these arguments are compelling, and therefore that no good reason has been given to accept IC

Bayesian Inference of Self-intention Attributed by Observer

Most of agents that learn policy for tasks with reinforcement learning (RL) lack the ability to communicate with people, which makes human-agent collaboration challenging. We believe that, in order for RL agents to comprehend utterances from human colleagues, RL agents must infer the mental states that people attribute to them because people sometimes infer an interlocutor's mental states and communicate on the basis of this mental inference. This paper proposes PublicSelf model, which is a model of a person who infers how the person's own behavior appears to their colleagues. We implemented the PublicSelf model for an RL agent in a simulated environment and examined the inference of the model by comparing it with people's judgment. The results showed that the agent's intention that people attributed to the agent's movement was correctly inferred by the model in scenes where people could find certain intentionality from the agent's behavior

Alert-BDI: BDI Model with Adaptive Alertness through Situational Awareness

In this paper, we address the problems faced by a group of agents that possess situational awareness, but lack a security mechanism, by the introduction of a adaptive risk management system. The Belief-Desire-Intention (BDI) architecture lacks a framework that would facilitate an adaptive risk management system that uses the situational awareness of the agents. We extend the BDI architecture with the concept of adaptive alertness. Agents can modify their level of alertness by monitoring the risks faced by them and by their peers. Alert-BDI enables the agents to detect and assess the risks faced by them in an efficient manner, thereby increasing operational efficiency and resistance against attacks.Comment: 14 pages, 3 figures. Submitted to ICACCI 2013, Mysore, Indi

Truth Predicates, Truth Bearers, and their Variants

This paper argues that truth predicates in natural language and their variants, predicates of correctness, satisfaction and validity, do not apply to propositions (not even with 'that'-clauses), but rather to a range of attitudinal and modal objects. As such natural language reflects a notion of truth that is primarily a normative notion of correctness constitutive of representational objects. The paper moreover argues that 'true' is part of a larger class of satisfaction predicates whose semantic differences are best accounted for in terms of a truthmaker theory along the lines of Fine's recent truthmaker semantics

Non-Epicurean Desires

In this paper, it is argued that there can be necessary and non-natural desires. After a discussion about what seems wrong with such desires, Epicurus’ classification of desires is treated similarly to Kripke’s treatment of the Kantian table of judgments. A sample of three cases is suggested to make this point

Reconciling Practical Knowledge with Self-Deception

Is it impossible for a person to do something intentionally without knowing that she is doing it? The phenomenon of self-deceived agency might seem to show otherwise. Here the agent is not lying, yet disavows a correct description of her intentional action. This disavowal might seem expressive of ignorance. However, I show that the self-deceived agent does know what she's doing. I argue that we should understand the factors that explain self-deception as masking rather than negating the practical knowledge characteristic of intentional action. This masking takes roughly the following form: when we are deceiving ourselves about what we are intentionally doing, we don't think about our action because it's painful to do so

Observation-based Model for BDI-Agents

We present a new computational model of BDI-agents, called the observation-based BDI-model. The key point of this BDI-model is to express agents' beliefs, desires and intentions as a set of runs (computing paths), which is exactly a system in the interpreted system model, a well-known agent model due to Halpern and his colleagues. Our BDI-model is computationally grounded in that we are able to associate the BDI-agent model with a computer program, and formulas, involving agents' beliefs, desires (goals) and intentions, can be understood as properties of program computations. We present a sound and complete proof system with respect to our BDI-model and explore how symbolic model checking techniques can be applied to model checking BDI-agents. In order to make our BDI-model more flexible and practically realistic, we generalize it so that agents can have multiple sources of beliefs, goals and intentions