On Automated Agents’ Rationality

2010

Will it be possible to create a self-aware and reasoning entity that has the capacity for decision making similar to that we ascribe to human beings? Modern agent systems, although used today in various applications wherever intelligence is required, are not ready for applications where human rationalities are usually the only option in making important decisions in critical or sensitive situations.

2000

In the decision-making and rationality research field, Rational Decision Theory (RDT) has always been the main framework, thanks to the elegance and complexity of its mathematical tools. Unfortunately, the formal refinement of the theory is not accompanied by a satisfying predictive accuracy, thus there is a big gap between what is predicted by the theory and the behavior of real subjects. Here we propose a new foundation of the RDT, which has to be based on a cognitive architecture for reason-based agents, acting on the basis of their beliefs in order to achieve their goals. In this perspective, the decision process is a cognitive evaluation of conflicting goals, based on different beliefs and values, but also on emotions and desires. We refer to a cognitive analysis of emotions and we integrate them in this more general RDT.

Mind & Society, 2006

In the decision-making and rationality research field, Rational Decision Theory (RDT) has always been the main framework, thanks to the elegance and complexity of its mathematical tools. Unfortunately, the formal refinement of the theory is not accompanied by a satisfying predictive accuracy, thus there is a big gap between what is predicted by the theory and the behavior of real subjects. Here we propose a new foundation of the RDT, which has to be based on a cognitive architecture for reason-based agents, acting on the basis of their beliefs in order to achieve their goals. In this perspective, the decision process is a cognitive evaluation of conflicting goals, based on different beliefs and values, but also on emotions and desires. We refer to a cognitive analysis of emotions and we integrate them in this more general RDT.

arXiv (Cornell University), 2020

Recent work in the behavioural sciences has begun to overturn the long-held belief that human decision making is irrational, suboptimal and subject to biases. This turn to the rational suggests that human decision making may be a better source of ideas for constraining how machine learning problems are defined than would otherwise be the case. One promising idea concerns human decision making that is dependent on apparently irrelevant aspects of the choice context. Previous work has shown that by taking into account choice context and making relational observations, people can maximize expected value. Other work has shown that Partially observable Markov decision processes (POMDPs) are a useful way to formulate human-like decision problems. Here, we propose a novel POMDP model for contextual choice tasks and show that, despite the apparent irrationalities, a reinforcement learner can take advantage of the way that humans make decisions. We suggest that human irrationalities may offer a productive source of inspiration for improving the design of AI architectures and machine learning methods. Preprint. Under review.

Journal of Economic Psychology, 2005

Herbert Simon has warned us that an explanatory account of human rationality must identify the significance of emotions for choice behavior. Customarily emphasizing the cognitive dimensions of decision making, relatively few researchers have paid close attention to specifying the complex ways in which emotion may shape human thinking and decisions. Accordingly, this paper is an attempt to follow Simon's suggestion and specify how emotions can enter into the theory of bounded rationality. To accomplish our task, we capitalize on Rom Harr e's work on causal powers, from which we propose a strategy to study the significance of emotion in decision-making processes. In an attempt to elaborate on an explanation of behavior by mechanism, we discuss a version of bounded rationality recently put forward by Gigerenzer, Todd, and the ABC Research Group [Simple Heuristics that Make us Smart, Oxford University Press, New York, 1999] and Gigerenzer and Selten [Bounded Rationality: The Adaptive Toolbox, MIT Press, Cambridge, MA, 2001, pp. 1-12], the so-called adaptive toolbox of fast and frugal heuristics. Coupled with insights from evolutionary psychology and neuroscience, this version of bounded rationality gives us a better grasp of the functional role of emotions within the human decision machinery.

1996

This paper presents an intr{gluction to a theory of rationalization for autonomous agents covering three kinds of rationalization: goal-bas~xl, belief based, and assumption-based rationalization. Wc show that the COMINT model of decisioa making (Botclho & ('oelho 1996) enables us to approach rationalization in a rather natural fashion. Namely, it presents an automatic (as opposed to deliberative and thoughtful) way of selecting the convenient sets of knowledgc structures to rationalize the agent's decisions. This automatic mechanism relies on the concepts of activation and association, both of which are central to the model of memory (Botelho & ('oelho 1995) underlying COMINT. We also give a fiwmal definition of a rationalization basis anti show how it is generated. Along the paper, wc discuss some examples of personnel selcction in organizations. Some aspects of the implementation of the model are also presented. Although rationalization serves a lot of purposes, this paper emphasizes its role in a multi-agent society.

Lecture Notes in Computer Science, 2010

Assistant Agents help ordinary people about computer tasks, in many ways, thanks to their rational reasoning capabilities about the current model of the world. However they face strong acceptability issues because of the lack of naturalness in their interaction with users. A promising approach is to provide Assistant Agents with a personality model and allow them to achieve behavioral reasoning in conjunction with rational reasoning. In this paper, we propose a formal framework to study the relationships between the rational and behavioral processes, based on the expression of the behaviors in terms of influence operators on the rational execution of actions and plans.

WSEAS Transactions on Computers, 2005

Intelligent behaviours have been traditionally considered exclusively as products of pure rationality processes. Considering other influential factors, such as emotions, has been accused of being "non scientific". However, pure rational processes fail when trying to explain most of human behaviours, in which emotion plays a key role. Nevertheless, emotional factors add an extra complexity to agent architectures, making them, hitherto, either few efficient or few reusable. This paper presents a context independent cognitive architecture for agents that combine emotions and rationality, named COGNITIVA, which bets on adaptivity as a weapon to fight against that complexity.

Proceedings of the AAAI Conference on Artificial Intelligence

Agency - the capacity to plan and act - and experience - the capacity to sense and feel - are two critical aspects that determine whether people will perceive non-human entities, such as autonomous agents, to have a mind. There is evidence that the absence of either can reduce cooperation. We present an experiment that tests the necessity of both for cooperation with agents. In this experiment we manipulated people's perceptions about the cognitive and affective abilities of agents, when engaging in the ultimatum game. The results indicated that people offered more money to agents that were perceived to make decisions according to their intentions (high agency), rather than randomly (low agency). Additionally, the results showed that people offered more money to agents that expressed emotion (high experience), when compared to agents that did not (low experience). We discuss the implications of this agency-experience theoretical framework for the design of artificially intellige...

2021

Humans exhibit irrational decision-making patterns in response to environmental triggers, such as experiencing an economic loss or gain. In this paper we investigate whether algorithms exhibit the same behavior by examining the observed decisions and latent risk and rationality parameters estimated by a random utility model with constant relative risk-aversion utility function. We use a dataset consisting of 10,000 hands of poker played by Pluribus, the first algorithm in the world to beat professional human players and find (1) Pluribus does shift its playing style in response to economic losses and gains, ceteris paribus; (2) Pluribus becomes more risk-averse and rational following a trigger but the humans become more risk-seeking and irrational; (3) the difference in playing styles between Pluribus and the humans on the dimensions of risk-aversion and rationality are particularly differentiable when both have experienced a trigger. This provides support that decision-making patte...