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Mind RACES: Project Objectives

The general goal of the Mind RACES project is to investigate different anticipatory cognitive mechanisms and architectures in order to build Cognitive Systems endowed with the ability to predict the outcome of their actions, to build a model of future events, to control their perception anticipating future stimuli and to emotionally react to possible future scenarios. Such Anticipatory Cognitive Systems will contribute to the successful implementation of the desired ambient intelligence.
To match this general goal the project has identified four distinct objectives which correspond to four different phases in the project.

On the basis of specific smart environments and multi-robot scenarios, the project (1) will identify typologies of problems in three cognitive functions sets (attention monitoring and control; goal directed behaviour, pro-activity and analogy; anticipatory emotions) which require different anticipatory cognitive capabilities.

The project (2) will improve existing anticipatory architectures and will incorporate missing anticipatory functionalities in them. The performances of these architectures will be tested in the previously identified scenarios.

In order to highlight relative strengths and weaknesses, the project (3) will compare in the same scenarios anticipatory architectures implemented from different theoretical background. In this phase we have the opportunity of evaluating if the translation of some mechanisms in other implementations show a different performance or if it leads to new side effects (that we can possibly exploit). This approach privileges (as in all the project) the cognitive function set over the concrete implementation and will give a relevant contribute to the theoretical foundation of the mechanisms, extracting the conceptual core from the constraints of the single implementations. At the same time, this leads us to the next phase.

Finally, the project (4) will design, implement and test in the scenarios the cognitive architectures that integrate anticipatory mechanisms from different cognitive function sets. Simulations and real robots will be used both to improve and compare single anticipatory models and to integrate them in the same cognitive architectures.

In what follows each of the four objectives is described and expected results and timing are listed.

Specification and implementation of scenarios

The first objective of the project is to identify 6 scenarios corresponding to the situation in which cognition requires anticipation and to implement 3 of them. This will allow to theoretically, logically and mathematically analyse and compare the different anticipatory mechanisms. The 6 scenarios will be specified crossing the following dimensions:

  • 3 cognitive functions sets: (1) Attention, Monitoring and Control; (2) Goal directed behaviour, Pro-activity and Analogy; (3) Anticipatory Emotions.

  • 2 categories of scenario: (1) Individual Domain; (2) Social Domain.

Expected results and timing:

  1. The complete specification of the scenarios concerning anticipation will be provided by Month 8 (D2.1)

  2. The implementation of the 3 scenarios will be completed by Month 12 (D2.2)

Design and implementation of simple anticipatory architectures

Within each cognitive functions set mentioned above, the project will consider the following anticipatory mechanisms:

  1. Attention, Monitoring and Control: expectation-based attention shifting, priming and context effects, attention as epistemic control, constructive perception.

  2. Goal directed behaviour, Pro-activity and Analogy: value anticipation, sub-symbolic planning, pro-active activation of goals, anticipation at different time scales and levels of abstractions, construction of models of future events based on analogy.

  3. Anticipatory Emotions: desires/goals activation based anticipatory affective states (somatic markers), attention attraction based on anticipatory affective states, affective monitoring of goals’ satisfaction, appraisal of future events on the basis of perceived signs.

The second objective of the project is to improve existing architectures which model a specific cognitive functions set with new mechanisms concerning the same cognitive functions set and test them in the 3 implemented scenarios At least two cognitive architectures will be tested by using real robots embedded in realistic scenarios.

The project will implement at least six improved architectures, 2 for each cognitive functions set. The following ones are some examples of the improvements that the project will pursue:

  • endow BDI (Beliefs, Desires, Intentions) architectures with pro-activity;

  • enhance the capacities of anticipatory neural network architectures and rule-based systems to predict at different time scales;

  • endow goal-directed systems with mechanisms based on analogy for building models of future events;

  • endow anticipatory systems with mechanisms for optimally predicting future events;

  • implement neural network architectures and anticipatory rule-based systems that predict on the basis of abstractions of states of the world, actions, or perception-action relations, as done by systems based on logic;

  • allow neural network systems to be goal-driven similarly to BDI systems;

  • endow BDI like systems or neural networks systems with the capacity of monitoring the environment and the capacity of shifting the attention;

  • endow either BDI like architectures or neural network architectures with mechanisms of goal activation depending on anticipatory affective states;

  • endow either BDI like architectures or Neural Networks architectures or rule-based systems with mechanisms of attention attraction depending on anticipatory affective states.

Expected results and timing:

  1. The improvement of theoretical models that deal with different cognitive functions sets will be provided by Month 8 (D3.1; D4.1;D5.1)

  2. The improvement and implementation of existing simple architectures that deal with different cognitive functions sets will be completed by Month 30 (D3.2; D4.2;D5.2)

Evaluation and Comparison of the different architectures and mechanisms

The third objective of the project is to evaluate and compare the improved models and architectures that implement each cognitive functions set.

To achieve an effective comparison we will define specific metrics for testing:

  1. the performances of single architectures in the scenarios;

  2. the adherence of an implemented architecture to its theoretical foundations;

  3. the practical relevance of a given architecture;

  4. the applicability of given theoretical model in a concrete implementation

Expected results and timing:

  1. The critical analysis and comparison of existing theoretical models will be completed by Month 8 (D3.1; D4.1;D5.1)

  2. The metrics and the complete specification and evaluation of the common methodology will be explicitly set by Month 18 (D2.3)

  3. The evaluation of the implemented simple architectures with respect to their theoretical models will be completed by Month 30 (D3.2; D4.2;D5.2)

  4. The test of the architectures in the scenarios and the comparison between different experimental results will be completed by Month 30 (D3.2; D4.2;D5.2)


The fourth objective of the project is to design and implement at least two cognitive architectures each integrating at least two anticipatory mechanisms concerning different cognitive functions sets. At least one of the integrated architectures will be a real robot. We will explore the interesting integrations on the basis of the possible positive complementarities between the simple mechanisms. The architectures of integration will provide a significant increase of performance with respect to architectures with anticipatory mechanisms concerning a single cognitive functions set. The integrated architectures will be tested against one of the three implemented scenarios depending on the kind of integration, or their variants. The following are some possible integrations (see the section below, “Enhancements of the state of the art”, for further details):

  • Integration between a deliberative and intentional control of behaviour and a rule-based (anticipatory) one.

  • Integration of epistemic actions (attention), anticipatory learning, and anticipatory behaviour where attention may shape learning, learning may shape anticipatory behaviour, and anticipatory behaviour again may shape attention.

  • Integration between context effects, formation of implicit and unconscious predictions, discovery of inconsistency, and shifting to deliberate level of cognition.

  • Integration between future-oriented low-level affective states, such as fear, its corresponding high-level cognitive affective states, and pro-active triggering of defensive goals based on negative affective states.

Expected results and timing:

  1. Integration of theoretical models will be completed by Month 13 (D6.1)

  2. Preliminary implementations and tests of integrated architectures will be provided by Month 25 (D6.2)

  3. Final implementations and tests of integrated architectures will be provided by Month 36 (D6.3)

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Last modified 2007-11-07 05:14 PM

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Anticipatory Cognitive Science is a research field that ensembles artificial intelligence, biology, psychology, neurology, engineering and philosophy in order to build anticipatory cognitive systems that are able to face human tasks with the same anticipatory capabilities and performance. In deep: Cognitive science is the interdisciplinary study of mind and intelligence, embracing philosophy, psychology, artificial intelligence, neuroscience, linguistics, and anthropology. Its intellectual origins are in the mid-1950s when researchers in several fields began to develop theories of mind based on complex representations and computational procedures. Its organizational origins are in the mid-1970s when the Cognitive Science Society was formed and the journal Cognitive Science began. Since then, more than sixty universities in North America, Europe, Asia, and Australia have established cognitive science programs, and many others have instituted courses in cognitive science.